Publications Conferences

Resistive Random Access Memory (RRAM) crossbar arrays are an attractive memory structure for emerging nonvolatile memory due to their high density and excellent scalability. Their ability to perform logic operations using RRAM devices makes them a critical component in non-von Neumann processing-in-memory architectures. Passive RRAM crossbar arrays (1-RRAM or 1R), however, suffer from a major issue of sneak path currents, leading to a lower readout margin and increasing write failures. To address this challenge, active RRAM arrays have been proposed, which incorporate a selector device in each memory cell (termed 1-selector-1-RRAM or 1S1R). The selector eliminates currents from unselected cells and therefore effectively mitigates the sneak path phenomenon. Yet, there is a need for a comprehensive analysis of 1S1R arrays, particularly concerning in-memory computation. In this paper, we introduce a 1S1R model tailored to a VO 2 -based selector and TiN/TiO x /HfO x /Pt RRAM device. We also present simulations of 1S1R arrays, incorporating all parasitic parameters, across a range of array sizes from 4 × 4 to 512 × 512. We evaluate the performance of Memristor-Aided Logic (MAGIC) gates in terms of switching delay, power consumption, and readout margin, and provide a comparative evaluation with passive 1R arrays.

Resistive Random Access Memory (RRAM) crossbar arrays are an attractive memory structure for emerging nonvolatile memory due to their high density and excellent scalability. Their ability to perform logic operations using RRAM devices makes them a critical component in non-von Neumann processing-in-memory architectures. Passive RRAM crossbar arrays (1-RRAM or 1R), however, suffer from a major issue of sneak path currents, leading to a lower readout margin and increasing write failures. To address this challenge, active RRAM arrays have been proposed, which incorporate a selector device in each memory cell (termed 1-selector-1-RRAM or 1S1R). The selector eliminates currents from unselected cells and therefore effectively mitigates the sneak path phenomenon. Yet, there is a need for a comprehensive analysis of 1S1R arrays, particularly concerning in-memory computation. In this paper, we introduce a 1S1R model tailored to a VO 2 -based selector and TiN/TiO x /HfO x /Pt RRAM device. We also present simulations of 1S1R arrays, incorporating all parasitic parameters, across a range of array sizes from 4 × 4 to 512 × 512. We evaluate the performance of Memristor-Aided Logic (MAGIC) gates in terms of switching delay, power consumption, and readout margin, and provide a comparative evaluation with passive 1R arrays.

Resistive Random Access Memory (RRAM) crossbar arrays are an attractive memory structure for emerging nonvolatile memory due to their high density and excellent scalability. Their ability to perform logic operations using RRAM devices makes them a critical component in non-von Neumann processing-in-memory architectures. Passive RRAM crossbar arrays (1-RRAM or 1R), however, suffer from a major issue of sneak path currents, leading to a lower readout margin and increasing write failures. To address this challenge, active RRAM arrays have been proposed, which incorporate a selector device in each memory cell (termed 1-selector-1-RRAM or 1S1R). The selector eliminates currents from unselected cells and therefore effectively mitigates the sneak path phenomenon. Yet, there is a need for a comprehensive analysis of 1S1R arrays, particularly concerning in-memory computation. In this paper, we introduce a 1S1R model tailored to a VO 2 -based selector and TiN/TiO x /HfO x /Pt RRAM device. We also present simulations of 1S1R arrays, incorporating all parasitic parameters, across a range of array sizes from 4 × 4 to 512 × 512. We evaluate the performance of Memristor-Aided Logic (MAGIC) gates in terms of switching delay, power consumption, and readout margin, and provide a comparative evaluation with passive 1R arrays.

Resistive Random Access Memory (RRAM) crossbar arrays are an attractive memory structure for emerging nonvolatile memory due to their high density and excellent scalability. Their ability to perform logic operations using RRAM devices makes them a critical component in non-von Neumann processing-in-memory architectures. Passive RRAM crossbar arrays (1-RRAM or 1R), however, suffer from a major issue of sneak path currents, leading to a lower readout margin and increasing write failures. To address this challenge, active RRAM arrays have been proposed, which incorporate a selector device in each memory cell (termed 1-selector-1-RRAM or 1S1R). The selector eliminates currents from unselected cells and therefore effectively mitigates the sneak path phenomenon. Yet, there is a need for a comprehensive analysis of 1S1R arrays, particularly concerning in-memory computation. In this paper, we introduce a 1S1R model tailored to a VO 2 -based selector and TiN/TiO x /HfO x /Pt RRAM device. We also present simulations of 1S1R arrays, incorporating all parasitic parameters, across a range of array sizes from 4 × 4 to 512 × 512. We evaluate the performance of Memristor-Aided Logic (MAGIC) gates in terms of switching delay, power consumption, and readout margin, and provide a comparative evaluation with passive 1R arrays.

Resistive Random Access Memory (RRAM) crossbar arrays are an attractive memory structure for emerging nonvolatile memory due to their high density and excellent scalability. Their ability to perform logic operations using RRAM devices makes them a critical component in non-von Neumann processing-in-memory architectures. Passive RRAM crossbar arrays (1-RRAM or 1R), however, suffer from a major issue of sneak path currents, leading to a lower readout margin and increasing write failures. To address this challenge, active RRAM arrays have been proposed, which incorporate a selector device in each memory cell (termed 1-selector-1-RRAM or 1S1R). The selector eliminates currents from unselected cells and therefore effectively mitigates the sneak path phenomenon. Yet, there is a need for a comprehensive analysis of 1S1R arrays, particularly concerning in-memory computation. In this paper, we introduce a 1S1R model tailored to a VO 2 -based selector and TiN/TiO x /HfO x /Pt RRAM device. We also present simulations of 1S1R arrays, incorporating all parasitic parameters, across a range of array sizes from 4 × 4 to 512 × 512. We evaluate the performance of Memristor-Aided Logic (MAGIC) gates in terms of switching delay, power consumption, and readout margin, and provide a comparative evaluation with passive 1R arrays.

Resistive Random Access Memory (RRAM) crossbar arrays are an attractive memory structure for emerging nonvolatile memory due to their high density and excellent scalability. Their ability to perform logic operations using RRAM devices makes them a critical component in non-von Neumann processing-in-memory architectures. Passive RRAM crossbar arrays (1-RRAM or 1R), however, suffer from a major issue of sneak path currents, leading to a lower readout margin and increasing write failures. To address this challenge, active RRAM arrays have been proposed, which incorporate a selector device in each memory cell (termed 1-selector-1-RRAM or 1S1R). The selector eliminates currents from unselected cells and therefore effectively mitigates the sneak path phenomenon. Yet, there is a need for a comprehensive analysis of 1S1R arrays, particularly concerning in-memory computation. In this paper, we introduce a 1S1R model tailored to a VO 2 -based selector and TiN/TiO x /HfO x /Pt RRAM device. We also present simulations of 1S1R arrays, incorporating all parasitic parameters, across a range of array sizes from 4 × 4 to 512 × 512. We evaluate the performance of Memristor-Aided Logic (MAGIC) gates in terms of switching delay, power consumption, and readout margin, and provide a comparative evaluation with passive 1R arrays.

Resistive Random Access Memory (RRAM) crossbar arrays are an attractive memory structure for emerging nonvolatile memory due to their high density and excellent scalability. Their ability to perform logic operations using RRAM devices makes them a critical component in non-von Neumann processing-in-memory architectures. Passive RRAM crossbar arrays (1-RRAM or 1R), however, suffer from a major issue of sneak path currents, leading to a lower readout margin and increasing write failures. To address this challenge, active RRAM arrays have been proposed, which incorporate a selector device in each memory cell (termed 1-selector-1-RRAM or 1S1R). The selector eliminates currents from unselected cells and therefore effectively mitigates the sneak path phenomenon. Yet, there is a need for a comprehensive analysis of 1S1R arrays, particularly concerning in-memory computation. In this paper, we introduce a 1S1R model tailored to a VO 2 -based selector and TiN/TiO x /HfO x /Pt RRAM device. We also present simulations of 1S1R arrays, incorporating all parasitic parameters, across a range of array sizes from 4 × 4 to 512 × 512. We evaluate the performance of Memristor-Aided Logic (MAGIC) gates in terms of switching delay, power consumption, and readout margin, and provide a comparative evaluation with passive 1R arrays.

Resistive Random Access Memory (RRAM) crossbar arrays are an attractive memory structure for emerging nonvolatile memory due to their high density and excellent scalability. Their ability to perform logic operations using RRAM devices makes them a critical component in non-von Neumann processing-in-memory architectures. Passive RRAM crossbar arrays (1-RRAM or 1R), however, suffer from a major issue of sneak path currents, leading to a lower readout margin and increasing write failures. To address this challenge, active RRAM arrays have been proposed, which incorporate a selector device in each memory cell (termed 1-selector-1-RRAM or 1S1R). The selector eliminates currents from unselected cells and therefore effectively mitigates the sneak path phenomenon. Yet, there is a need for a comprehensive analysis of 1S1R arrays, particularly concerning in-memory computation. In this paper, we introduce a 1S1R model tailored to a VO 2 -based selector and TiN/TiO x /HfO x /Pt RRAM device. We also present simulations of 1S1R arrays, incorporating all parasitic parameters, across a range of array sizes from 4 × 4 to 512 × 512. We evaluate the performance of Memristor-Aided Logic (MAGIC) gates in terms of switching delay, power consumption, and readout margin, and provide a comparative evaluation with passive 1R arrays.

Resistive Random Access Memory (RRAM) crossbar arrays are an attractive memory structure for emerging nonvolatile memory due to their high density and excellent scalability. Their ability to perform logic operations using RRAM devices makes them a critical component in non-von Neumann processing-in-memory architectures. Passive RRAM crossbar arrays (1-RRAM or 1R), however, suffer from a major issue of sneak path currents, leading to a lower readout margin and increasing write failures. To address this challenge, active RRAM arrays have been proposed, which incorporate a selector device in each memory cell (termed 1-selector-1-RRAM or 1S1R). The selector eliminates currents from unselected cells and therefore effectively mitigates the sneak path phenomenon. Yet, there is a need for a comprehensive analysis of 1S1R arrays, particularly concerning in-memory computation. In this paper, we introduce a 1S1R model tailored to a VO 2 -based selector and TiN/TiO x /HfO x /Pt RRAM device. We also present simulations of 1S1R arrays, incorporating all parasitic parameters, across a range of array sizes from 4 × 4 to 512 × 512. We evaluate the performance of Memristor-Aided Logic (MAGIC) gates in terms of switching delay, power consumption, and readout margin, and provide a comparative evaluation with passive 1R arrays.

Stateful logic is a promising processing-in-memory (PIM) paradigm to perform logic operations using emerging nonvolatile memory cells. While most stateful logic circuits to date focused on technologies such as resistive RAM, we propose two approaches to designing stateful logic using spinorbit torque (SOT) MRAM. The first approach utilizes the separation of read and write paths in SOT devices to perform logic operations. In contrast to previous work, our method utilizes a standard memory structure, and each row can be used as input or output. The second approach uses voltage-gated SOT switching to allow stateful logic in denser memory arrays.

We present array structures to support the two approaches and evaluate their functionality using SPICE simulations in the presence of process variation and device mismatch.

Recent research has sought to accelerate cryptographic hash functions as they are at the core of modern cryptography. Traditional designs, however, suffer from the von Neumann bottleneck that originates from the separation of processing and memory units. An emerging solution to overcome this bottleneck is processing-in-memory (PIM): performing logic
within the same devices responsible for memory to eliminate data-transfer and simultaneously provide massive computational parallelism. In this paper, we seek to vastly accelerate the stateof-the-art SHA-3 cryptographic function using the memristive memory processing unit (mMPU), a general-purpose memristive PIM architecture. To that end, we propose a novel in-memory algorithm for variable rotation, and utilize an efficient mapping of the SHA-3 state vector for memristive crossbar arrays to efficiently exploit PIM parallelism. We demonstrate a massive energy efficiency of 1, 422 Gbps/W, improving a state-of-the-art memristive SHA-3 accelerator (SHINE-2) by 4.6×.

The emerging memristive Memory Processing Unit (mMPU) overcomes the memory wall through memristive devices that unite storage and logic for real processing-in-memory (PIM)
systems. At the core of the mMPU is stateful logic, which is accelerated with memristive partitions to enable logic with massive inherent parallelism within crossbar arrays. This paper
vastly accelerates the fundamental operations of matrix-vector multiplication and convolution in the mMPU, with either fullprecision or binary elements. These proposed algorithms establish an efficient foundation for large-scale mMPU applications such as neural-networks, image processing, and numerical methods.

We overcome the inherent asymmetry limitation in the previous in-memory full-precision matrix-vector multiplication solutions by utilizing techniques from block matrix multiplication and reduction. We present the first fast in-memory binary matrixvector multiplication algorithm by utilizing memristive partitions with a tree-based popcount reduction (39× faster than previous work). For convolution, we present a novel in-memory inputparallel concept which we utilize for a full-precision algorithm that overcomes the asymmetry limitation in convolution, while also improving latency (2× faster than previous work), and the first fast binary algorithm (12× faster than previous work).

The emerging memristive Memory Processing Unit (mMPU) overcomes the memory wall through memristive devices that unite storage and logic for real processing-in-memory (PIM)
systems. At the core of the mMPU is stateful logic, which is accelerated with memristive partitions to enable logic with massive inherent parallelism within crossbar arrays. This paper
vastly accelerates the fundamental operations of matrix-vector multiplication and convolution in the mMPU, with either fullprecision or binary elements. These proposed algorithms establish an efficient foundation for large-scale mMPU applications such as neural-networks, image processing, and numerical methods.

We overcome the inherent asymmetry limitation in the previous in-memory full-precision matrix-vector multiplication solutions by utilizing techniques from block matrix multiplication and reduction. We present the first fast in-memory binary matrixvector multiplication algorithm by utilizing memristive partitions with a tree-based popcount reduction (39× faster than previous work). For convolution, we present a novel in-memory inputparallel concept which we utilize for a full-precision algorithm that overcomes the asymmetry limitation in convolution, while also improving latency (2× faster than previous work), and the first fast binary algorithm (12× faster than previous work).

The emerging memristive Memory Processing Unit (mMPU) overcomes the memory wall through memristive devices that unite storage and logic for real processing-in-memory (PIM)
systems. At the core of the mMPU is stateful logic, which is accelerated with memristive partitions to enable logic with massive inherent parallelism within crossbar arrays. This paper
vastly accelerates the fundamental operations of matrix-vector multiplication and convolution in the mMPU, with either fullprecision or binary elements. These proposed algorithms establish an efficient foundation for large-scale mMPU applications such as neural-networks, image processing, and numerical methods.

We overcome the inherent asymmetry limitation in the previous in-memory full-precision matrix-vector multiplication solutions by utilizing techniques from block matrix multiplication and reduction. We present the first fast in-memory binary matrixvector multiplication algorithm by utilizing memristive partitions with a tree-based popcount reduction (39× faster than previous work). For convolution, we present a novel in-memory inputparallel concept which we utilize for a full-precision algorithm that overcomes the asymmetry limitation in convolution, while also improving latency (2× faster than previous work), and the first fast binary algorithm (12× faster than previous work).

The emerging memristive Memory Processing Unit (mMPU) overcomes the memory wall through memristive devices that unite storage and logic for real processing-in-memory (PIM)
systems. At the core of the mMPU is stateful logic, which is accelerated with memristive partitions to enable logic with massive inherent parallelism within crossbar arrays. This paper
vastly accelerates the fundamental operations of matrix-vector multiplication and convolution in the mMPU, with either fullprecision or binary elements. These proposed algorithms establish an efficient foundation for large-scale mMPU applications such as neural-networks, image processing, and numerical methods.

We overcome the inherent asymmetry limitation in the previous in-memory full-precision matrix-vector multiplication solutions by utilizing techniques from block matrix multiplication and reduction. We present the first fast in-memory binary matrixvector multiplication algorithm by utilizing memristive partitions with a tree-based popcount reduction (39× faster than previous work). For convolution, we present a novel in-memory inputparallel concept which we utilize for a full-precision algorithm that overcomes the asymmetry limitation in convolution, while also improving latency (2× faster than previous work), and the first fast binary algorithm (12× faster than previous work).

The emerging memristive Memory Processing Unit (mMPU) overcomes the memory wall through memristive devices that unite storage and logic for real processing-in-memory (PIM)
systems. At the core of the mMPU is stateful logic, which is accelerated with memristive partitions to enable logic with massive inherent parallelism within crossbar arrays. This paper
vastly accelerates the fundamental operations of matrix-vector multiplication and convolution in the mMPU, with either fullprecision or binary elements. These proposed algorithms establish an efficient foundation for large-scale mMPU applications such as neural-networks, image processing, and numerical methods.

We overcome the inherent asymmetry limitation in the previous in-memory full-precision matrix-vector multiplication solutions by utilizing techniques from block matrix multiplication and reduction. We present the first fast in-memory binary matrixvector multiplication algorithm by utilizing memristive partitions with a tree-based popcount reduction (39× faster than previous work). For convolution, we present a novel in-memory inputparallel concept which we utilize for a full-precision algorithm that overcomes the asymmetry limitation in convolution, while also improving latency (2× faster than previous work), and the first fast binary algorithm (12× faster than previous work).

The emerging memristive Memory Processing Unit (mMPU) overcomes the memory wall through memristive devices that unite storage and logic for real processing-in-memory (PIM)
systems. At the core of the mMPU is stateful logic, which is accelerated with memristive partitions to enable logic with massive inherent parallelism within crossbar arrays. This paper
vastly accelerates the fundamental operations of matrix-vector multiplication and convolution in the mMPU, with either fullprecision or binary elements. These proposed algorithms establish an efficient foundation for large-scale mMPU applications such as neural-networks, image processing, and numerical methods.

We overcome the inherent asymmetry limitation in the previous in-memory full-precision matrix-vector multiplication solutions by utilizing techniques from block matrix multiplication and reduction. We present the first fast in-memory binary matrixvector multiplication algorithm by utilizing memristive partitions with a tree-based popcount reduction (39× faster than previous work). For convolution, we present a novel in-memory inputparallel concept which we utilize for a full-precision algorithm that overcomes the asymmetry limitation in convolution, while also improving latency (2× faster than previous work), and the first fast binary algorithm (12× faster than previous work).

The emerging memristive Memory Processing Unit (mMPU) overcomes the memory wall through memristive devices that unite storage and logic for real processing-in-memory (PIM)
systems. At the core of the mMPU is stateful logic, which is accelerated with memristive partitions to enable logic with massive inherent parallelism within crossbar arrays. This paper
vastly accelerates the fundamental operations of matrix-vector multiplication and convolution in the mMPU, with either fullprecision or binary elements. These proposed algorithms establish an efficient foundation for large-scale mMPU applications such as neural-networks, image processing, and numerical methods.

We overcome the inherent asymmetry limitation in the previous in-memory full-precision matrix-vector multiplication solutions by utilizing techniques from block matrix multiplication and reduction. We present the first fast in-memory binary matrixvector multiplication algorithm by utilizing memristive partitions with a tree-based popcount reduction (39× faster than previous work). For convolution, we present a novel in-memory inputparallel concept which we utilize for a full-precision algorithm that overcomes the asymmetry limitation in convolution, while also improving latency (2× faster than previous work), and the first fast binary algorithm (12× faster than previous work).

The emerging memristive Memory Processing Unit (mMPU) overcomes the memory wall through memristive devices that unite storage and logic for real processing-in-memory (PIM)
systems. At the core of the mMPU is stateful logic, which is accelerated with memristive partitions to enable logic with massive inherent parallelism within crossbar arrays. This paper
vastly accelerates the fundamental operations of matrix-vector multiplication and convolution in the mMPU, with either fullprecision or binary elements. These proposed algorithms establish an efficient foundation for large-scale mMPU applications such as neural-networks, image processing, and numerical methods.

We overcome the inherent asymmetry limitation in the previous in-memory full-precision matrix-vector multiplication solutions by utilizing techniques from block matrix multiplication and reduction. We present the first fast in-memory binary matrixvector multiplication algorithm by utilizing memristive partitions with a tree-based popcount reduction (39× faster than previous work). For convolution, we present a novel in-memory inputparallel concept which we utilize for a full-precision algorithm that overcomes the asymmetry limitation in convolution, while also improving latency (2× faster than previous work), and the first fast binary algorithm (12× faster than previous work).

The emerging memristive Memory Processing Unit (mMPU) overcomes the memory wall through memristive devices that unite storage and logic for real processing-in-memory (PIM)
systems. At the core of the mMPU is stateful logic, which is accelerated with memristive partitions to enable logic with massive inherent parallelism within crossbar arrays. This paper
vastly accelerates the fundamental operations of matrix-vector multiplication and convolution in the mMPU, with either fullprecision or binary elements. These proposed algorithms establish an efficient foundation for large-scale mMPU applications such as neural-networks, image processing, and numerical methods.

We overcome the inherent asymmetry limitation in the previous in-memory full-precision matrix-vector multiplication solutions by utilizing techniques from block matrix multiplication and reduction. We present the first fast in-memory binary matrixvector multiplication algorithm by utilizing memristive partitions with a tree-based popcount reduction (39× faster than previous work). For convolution, we present a novel in-memory inputparallel concept which we utilize for a full-precision algorithm that overcomes the asymmetry limitation in convolution, while also improving latency (2× faster than previous work), and the first fast binary algorithm (12× faster than previous work).

The emerging memristive Memory Processing Unit (mMPU) overcomes the memory wall through memristive devices that unite storage and logic for real processing-in-memory (PIM)
systems. At the core of the mMPU is stateful logic, which is accelerated with memristive partitions to enable logic with massive inherent parallelism within crossbar arrays. This paper
vastly accelerates the fundamental operations of matrix-vector multiplication and convolution in the mMPU, with either fullprecision or binary elements. These proposed algorithms establish an efficient foundation for large-scale mMPU applications such as neural-networks, image processing, and numerical methods.

We overcome the inherent asymmetry limitation in the previous in-memory full-precision matrix-vector multiplication solutions by utilizing techniques from block matrix multiplication and reduction. We present the first fast in-memory binary matrixvector multiplication algorithm by utilizing memristive partitions with a tree-based popcount reduction (39× faster than previous work). For convolution, we present a novel in-memory inputparallel concept which we utilize for a full-precision algorithm that overcomes the asymmetry limitation in convolution, while also improving latency (2× faster than previous work), and the first fast binary algorithm (12× faster than previous work).

The emerging memristive Memory Processing Unit (mMPU) overcomes the memory wall through memristive devices that unite storage and logic for real processing-in-memory (PIM)
systems. At the core of the mMPU is stateful logic, which is accelerated with memristive partitions to enable logic with massive inherent parallelism within crossbar arrays. This paper
vastly accelerates the fundamental operations of matrix-vector multiplication and convolution in the mMPU, with either fullprecision or binary elements. These proposed algorithms establish an efficient foundation for large-scale mMPU applications such as neural-networks, image processing, and numerical methods.

We overcome the inherent asymmetry limitation in the previous in-memory full-precision matrix-vector multiplication solutions by utilizing techniques from block matrix multiplication and reduction. We present the first fast in-memory binary matrixvector multiplication algorithm by utilizing memristive partitions with a tree-based popcount reduction (39× faster than previous work). For convolution, we present a novel in-memory inputparallel concept which we utilize for a full-precision algorithm that overcomes the asymmetry limitation in convolution, while also improving latency (2× faster than previous work), and the first fast binary algorithm (12× faster than previous work).

The emerging memristive Memory Processing Unit (mMPU) overcomes the memory wall through memristive devices that unite storage and logic for real processing-in-memory (PIM)
systems. At the core of the mMPU is stateful logic, which is accelerated with memristive partitions to enable logic with massive inherent parallelism within crossbar arrays. This paper
vastly accelerates the fundamental operations of matrix-vector multiplication and convolution in the mMPU, with either fullprecision or binary elements. These proposed algorithms establish an efficient foundation for large-scale mMPU applications such as neural-networks, image processing, and numerical methods.

We overcome the inherent asymmetry limitation in the previous in-memory full-precision matrix-vector multiplication solutions by utilizing techniques from block matrix multiplication and reduction. We present the first fast in-memory binary matrixvector multiplication algorithm by utilizing memristive partitions with a tree-based popcount reduction (39× faster than previous work). For convolution, we present a novel in-memory inputparallel concept which we utilize for a full-precision algorithm that overcomes the asymmetry limitation in convolution, while also improving latency (2× faster than previous work), and the first fast binary algorithm (12× faster than previous work).

The growing demand for multi-band and multi-standard wireless devices requires flexible architectures that can reutilize the different blocks in the RF chains to reduce size and power consumption. The traditional multi-band radio with an RF chain per band is no longer scalable. Memristive devices have shown excellent performance as RF switches and also have a small footprint. Furthermore, as they are fabricated in the back-end of line of CMOS process, they enable tunability to integrated spiral inductors. In this paper, we present the design and simulations of a dual-band (2.4 GHz and 5 GHz) source degenerated low-noise amplifier (LNA) using memristive-via switched tunable inductors. Owing to the tunable inductor, the dual-band LNA has negligible area overhead compared to its single-band sibling. The LNA is designed using a 0.18-um RF CMOS technology and achieves a gain of 18.8 dB and 10.3 dB at 2.4 GHz and 5 GHz, respectively, and a noise figure (NF) below 2.3 dB at both bands. In addition, we present a semi-automated design methodology for the tunable inductors.

The growing demand for multi-band and multi-standard wireless devices requires flexible architectures that can reutilize the different blocks in the RF chains to reduce size and power consumption. The traditional multi-band radio with an RF chain per band is no longer scalable. Memristive devices have shown excellent performance as RF switches and also have a small footprint. Furthermore, as they are fabricated in the back-end of line of CMOS process, they enable tunability to integrated spiral inductors. In this paper, we present the design and simulations of a dual-band (2.4 GHz and 5 GHz) source degenerated low-noise amplifier (LNA) using memristive-via switched tunable inductors. Owing to the tunable inductor, the dual-band LNA has negligible area overhead compared to its single-band sibling. The LNA is designed using a 0.18-um RF CMOS technology and achieves a gain of 18.8 dB and 10.3 dB at 2.4 GHz and 5 GHz, respectively, and a noise figure (NF) below 2.3 dB at both bands. In addition, we present a semi-automated design methodology for the tunable inductors.

The emerging memristive Memory Processing Unit (mMPU) overcomes the memory wall through memristive devices that unite storage and logic for real processing-in-memory (PIM)
systems. At the core of the mMPU is stateful logic, which is accelerated with memristive partitions to enable logic with massive inherent parallelism within crossbar arrays. This paper
vastly accelerates the fundamental operations of matrix-vector multiplication and convolution in the mMPU, with either fullprecision or binary elements. These proposed algorithms establish an efficient foundation for large-scale mMPU applications such as neural-networks, image processing, and numerical methods.

We overcome the inherent asymmetry limitation in the previous in-memory full-precision matrix-vector multiplication solutions by utilizing techniques from block matrix multiplication and reduction. We present the first fast in-memory binary matrixvector multiplication algorithm by utilizing memristive partitions with a tree-based popcount reduction (39× faster than previous work). For convolution, we present a novel in-memory inputparallel concept which we utilize for a full-precision algorithm that overcomes the asymmetry limitation in convolution, while also improving latency (2× faster than previous work), and the first fast binary algorithm (12× faster than previous work).

The emerging memristive Memory Processing Unit (mMPU) overcomes the memory wall through memristive devices that unite storage and logic for real processing-in-memory (PIM)
systems. At the core of the mMPU is stateful logic, which is accelerated with memristive partitions to enable logic with massive inherent parallelism within crossbar arrays. This paper
vastly accelerates the fundamental operations of matrix-vector multiplication and convolution in the mMPU, with either fullprecision or binary elements. These proposed algorithms establish an efficient foundation for large-scale mMPU applications such as neural-networks, image processing, and numerical methods.

We overcome the inherent asymmetry limitation in the previous in-memory full-precision matrix-vector multiplication solutions by utilizing techniques from block matrix multiplication and reduction. We present the first fast in-memory binary matrixvector multiplication algorithm by utilizing memristive partitions with a tree-based popcount reduction (39× faster than previous work). For convolution, we present a novel in-memory inputparallel concept which we utilize for a full-precision algorithm that overcomes the asymmetry limitation in convolution, while also improving latency (2× faster than previous work), and the first fast binary algorithm (12× faster than previous work).

The growing demand for multi-band and multi-standard wireless devices requires flexible architectures that can reutilize the different blocks in the RF chains to reduce size and power consumption. The traditional multi-band radio with an RF chain per band is no longer scalable. Memristive devices have shown excellent performance as RF switches and also have a small footprint. Furthermore, as they are fabricated in the back-end of line of CMOS process, they enable tunability to integrated spiral inductors. In this paper, we present the design and simulations of a dual-band (2.4 GHz and 5 GHz) source degenerated low-noise amplifier (LNA) using memristive-via switched tunable inductors. Owing to the tunable inductor, the dual-band LNA has negligible area overhead compared to its single-band sibling. The LNA is designed using a 0.18-um RF CMOS technology and achieves a gain of 18.8 dB and 10.3 dB at 2.4 GHz and 5 GHz, respectively, and a noise figure (NF) below 2.3 dB at both bands. In addition, we present a semi-automated design methodology for the tunable inductors.

The growing demand for multi-band and multi-standard wireless devices requires flexible architectures that can reutilize the different blocks in the RF chains to reduce size and power consumption. The traditional multi-band radio with an RF chain per band is no longer scalable. Memristive devices have shown excellent performance as RF switches and also have a small footprint. Furthermore, as they are fabricated in the back-end of line of CMOS process, they enable tunability to integrated spiral inductors. In this paper, we present the design and simulations of a dual-band (2.4 GHz and 5 GHz) source degenerated low-noise amplifier (LNA) using memristive-via switched tunable inductors. Owing to the tunable inductor, the dual-band LNA has negligible area overhead compared to its single-band sibling. The LNA is designed using a 0.18-um RF CMOS technology and achieves a gain of 18.8 dB and 10.3 dB at 2.4 GHz and 5 GHz, respectively, and a noise figure (NF) below 2.3 dB at both bands. In addition, we present a semi-automated design methodology for the tunable inductors.

The emerging memristive Memory Processing Unit (mMPU) overcomes the memory wall through memristive devices that unite storage and logic for real processing-in-memory (PIM)
systems. At the core of the mMPU is stateful logic, which is accelerated with memristive partitions to enable logic with massive inherent parallelism within crossbar arrays. This paper
vastly accelerates the fundamental operations of matrix-vector multiplication and convolution in the mMPU, with either fullprecision or binary elements. These proposed algorithms establish an efficient foundation for large-scale mMPU applications such as neural-networks, image processing, and numerical methods.

We overcome the inherent asymmetry limitation in the previous in-memory full-precision matrix-vector multiplication solutions by utilizing techniques from block matrix multiplication and reduction. We present the first fast in-memory binary matrixvector multiplication algorithm by utilizing memristive partitions with a tree-based popcount reduction (39× faster than previous work). For convolution, we present a novel in-memory inputparallel concept which we utilize for a full-precision algorithm that overcomes the asymmetry limitation in convolution, while also improving latency (2× faster than previous work), and the first fast binary algorithm (12× faster than previous work).

The emerging memristive Memory Processing Unit (mMPU) overcomes the memory wall through memristive devices that unite storage and logic for real processing-in-memory (PIM)
systems. At the core of the mMPU is stateful logic, which is accelerated with memristive partitions to enable logic with massive inherent parallelism within crossbar arrays. This paper
vastly accelerates the fundamental operations of matrix-vector multiplication and convolution in the mMPU, with either fullprecision or binary elements. These proposed algorithms establish an efficient foundation for large-scale mMPU applications such as neural-networks, image processing, and numerical methods.

We overcome the inherent asymmetry limitation in the previous in-memory full-precision matrix-vector multiplication solutions by utilizing techniques from block matrix multiplication and reduction. We present the first fast in-memory binary matrixvector multiplication algorithm by utilizing memristive partitions with a tree-based popcount reduction (39× faster than previous work). For convolution, we present a novel in-memory inputparallel concept which we utilize for a full-precision algorithm that overcomes the asymmetry limitation in convolution, while also improving latency (2× faster than previous work), and the first fast binary algorithm (12× faster than previous work).

The growing demand for multi-band and multi-standard wireless devices requires flexible architectures that can reutilize the different blocks in the RF chains to reduce size and power consumption. The traditional multi-band radio with an RF chain per band is no longer scalable. Memristive devices have shown excellent performance as RF switches and also have a small footprint. Furthermore, as they are fabricated in the back-end of line of CMOS process, they enable tunability to integrated spiral inductors. In this paper, we present the design and simulations of a dual-band (2.4 GHz and 5 GHz) source degenerated low-noise amplifier (LNA) using memristive-via switched tunable inductors. Owing to the tunable inductor, the dual-band LNA has negligible area overhead compared to its single-band sibling. The LNA is designed using a 0.18-um RF CMOS technology and achieves a gain of 18.8 dB and 10.3 dB at 2.4 GHz and 5 GHz, respectively, and a noise figure (NF) below 2.3 dB at both bands. In addition, we present a semi-automated design methodology for the tunable inductors.

The growing demand for multi-band and multi-standard wireless devices requires flexible architectures that can reutilize the different blocks in the RF chains to reduce size and power consumption. The traditional multi-band radio with an RF chain per band is no longer scalable. Memristive devices have shown excellent performance as RF switches and also have a small footprint. Furthermore, as they are fabricated in the back-end of line of CMOS process, they enable tunability to integrated spiral inductors. In this paper, we present the design and simulations of a dual-band (2.4 GHz and 5 GHz) source degenerated low-noise amplifier (LNA) using memristive-via switched tunable inductors. Owing to the tunable inductor, the dual-band LNA has negligible area overhead compared to its single-band sibling. The LNA is designed using a 0.18-um RF CMOS technology and achieves a gain of 18.8 dB and 10.3 dB at 2.4 GHz and 5 GHz, respectively, and a noise figure (NF) below 2.3 dB at both bands. In addition, we present a semi-automated design methodology for the tunable inductors.

The growing demand for multi-band and multi-standard wireless devices requires flexible architectures that can reutilize the different blocks in the RF chains to reduce size and power consumption. The traditional multi-band radio with an RF chain per band is no longer scalable. Memristive devices have shown excellent performance as RF switches and also have a small footprint. Furthermore, as they are fabricated in the back-end of line of CMOS process, they enable tunability to integrated spiral inductors. In this paper, we present the design and simulations of a dual-band (2.4 GHz and 5 GHz) source degenerated low-noise amplifier (LNA) using memristive-via switched tunable inductors. Owing to the tunable inductor, the dual-band LNA has negligible area overhead compared to its single-band sibling. The LNA is designed using a 0.18-um RF CMOS technology and achieves a gain of 18.8 dB and 10.3 dB at 2.4 GHz and 5 GHz, respectively, and a noise figure (NF) below 2.3 dB at both bands. In addition, we present a semi-automated design methodology for the tunable inductors.

The growing demand for multi-band and multi-standard wireless devices requires flexible architectures that can reutilize the different blocks in the RF chains to reduce size and power consumption. The traditional multi-band radio with an RF chain per band is no longer scalable. Memristive devices have shown excellent performance as RF switches and also have a small footprint. Furthermore, as they are fabricated in the back-end of line of CMOS process, they enable tunability to integrated spiral inductors. In this paper, we present the design and simulations of a dual-band (2.4 GHz and 5 GHz) source degenerated low-noise amplifier (LNA) using memristive-via switched tunable inductors. Owing to the tunable inductor, the dual-band LNA has negligible area overhead compared to its single-band sibling. The LNA is designed using a 0.18-um RF CMOS technology and achieves a gain of 18.8 dB and 10.3 dB at 2.4 GHz and 5 GHz, respectively, and a noise figure (NF) below 2.3 dB at both bands. In addition, we present a semi-automated design methodology for the tunable inductors.

The growing demand for multi-band and multi-standard wireless devices requires flexible architectures that can reutilize the different blocks in the RF chains to reduce size and power consumption. The traditional multi-band radio with an RF chain per band is no longer scalable. Memristive devices have shown excellent performance as RF switches and also have a small footprint. Furthermore, as they are fabricated in the back-end of line of CMOS process, they enable tunability to integrated spiral inductors. In this paper, we present the design and simulations of a dual-band (2.4 GHz and 5 GHz) source degenerated low-noise amplifier (LNA) using memristive-via switched tunable inductors. Owing to the tunable inductor, the dual-band LNA has negligible area overhead compared to its single-band sibling. The LNA is designed using a 0.18-um RF CMOS technology and achieves a gain of 18.8 dB and 10.3 dB at 2.4 GHz and 5 GHz, respectively, and a noise figure (NF) below 2.3 dB at both bands. In addition, we present a semi-automated design methodology for the tunable inductors.

The growing demand for multi-band and multi-standard wireless devices requires flexible architectures that can reutilize the different blocks in the RF chains to reduce size and power consumption. The traditional multi-band radio with an RF chain per band is no longer scalable. Memristive devices have shown excellent performance as RF switches and also have a small footprint. Furthermore, as they are fabricated in the back-end of line of CMOS process, they enable tunability to integrated spiral inductors. In this paper, we present the design and simulations of a dual-band (2.4 GHz and 5 GHz) source degenerated low-noise amplifier (LNA) using memristive-via switched tunable inductors. Owing to the tunable inductor, the dual-band LNA has negligible area overhead compared to its single-band sibling. The LNA is designed using a 0.18-um RF CMOS technology and achieves a gain of 18.8 dB and 10.3 dB at 2.4 GHz and 5 GHz, respectively, and a noise figure (NF) below 2.3 dB at both bands. In addition, we present a semi-automated design methodology for the tunable inductors.

The growing demand for multi-band and multi-standard wireless devices requires flexible architectures that can reutilize the different blocks in the RF chains to reduce size and power consumption. The traditional multi-band radio with an RF chain per band is no longer scalable. Memristive devices have shown excellent performance as RF switches and also have a small footprint. Furthermore, as they are fabricated in the back-end of line of CMOS process, they enable tunability to integrated spiral inductors. In this paper, we present the design and simulations of a dual-band (2.4 GHz and 5 GHz) source degenerated low-noise amplifier (LNA) using memristive-via switched tunable inductors. Owing to the tunable inductor, the dual-band LNA has negligible area overhead compared to its single-band sibling. The LNA is designed using a 0.18-um RF CMOS technology and achieves a gain of 18.8 dB and 10.3 dB at 2.4 GHz and 5 GHz, respectively, and a noise figure (NF) below 2.3 dB at both bands. In addition, we present a semi-automated design methodology for the tunable inductors.

The growing demand for multi-band and multi-standard wireless devices requires flexible architectures that can reutilize the different blocks in the RF chains to reduce size and power consumption. The traditional multi-band radio with an RF chain per band is no longer scalable. Memristive devices have shown excellent performance as RF switches and also have a small footprint. Furthermore, as they are fabricated in the back-end of line of CMOS process, they enable tunability to integrated spiral inductors. In this paper, we present the design and simulations of a dual-band (2.4 GHz and 5 GHz) source degenerated low-noise amplifier (LNA) using memristive-via switched tunable inductors. Owing to the tunable inductor, the dual-band LNA has negligible area overhead compared to its single-band sibling. The LNA is designed using a 0.18-um RF CMOS technology and achieves a gain of 18.8 dB and 10.3 dB at 2.4 GHz and 5 GHz, respectively, and a noise figure (NF) below 2.3 dB at both bands. In addition, we present a semi-automated design methodology for the tunable inductors.

The growing demand for multi-band and multi-standard wireless devices requires flexible architectures that can reutilize the different blocks in the RF chains to reduce size and power consumption. The traditional multi-band radio with an RF chain per band is no longer scalable. Memristive devices have shown excellent performance as RF switches and also have a small footprint. Furthermore, as they are fabricated in the back-end of line of CMOS process, they enable tunability to integrated spiral inductors. In this paper, we present the design and simulations of a dual-band (2.4 GHz and 5 GHz) source degenerated low-noise amplifier (LNA) using memristive-via switched tunable inductors. Owing to the tunable inductor, the dual-band LNA has negligible area overhead compared to its single-band sibling. The LNA is designed using a 0.18-um RF CMOS technology and achieves a gain of 18.8 dB and 10.3 dB at 2.4 GHz and 5 GHz, respectively, and a noise figure (NF) below 2.3 dB at both bands. In addition, we present a semi-automated design methodology for the tunable inductors.

The growing demand for multi-band and multi-standard wireless devices requires flexible architectures that can reutilize the different blocks in the RF chains to reduce size and power consumption. The traditional multi-band radio with an RF chain per band is no longer scalable. Memristive devices have shown excellent performance as RF switches and also have a small footprint. Furthermore, as they are fabricated in the back-end of line of CMOS process, they enable tunability to integrated spiral inductors. In this paper, we present the design and simulations of a dual-band (2.4 GHz and 5 GHz) source degenerated low-noise amplifier (LNA) using memristive-via switched tunable inductors. Owing to the tunable inductor, the dual-band LNA has negligible area overhead compared to its single-band sibling. The LNA is designed using a 0.18-um RF CMOS technology and achieves a gain of 18.8 dB and 10.3 dB at 2.4 GHz and 5 GHz, respectively, and a noise figure (NF) below 2.3 dB at both bands. In addition, we present a semi-automated design methodology for the tunable inductors.

The growing demand for multi-band and multi-standard wireless devices requires flexible architectures that can reutilize the different blocks in the RF chains to reduce size and power consumption. The traditional multi-band radio with an RF chain per band is no longer scalable. Memristive devices have shown excellent performance as RF switches and also have a small footprint. Furthermore, as they are fabricated in the back-end of line of CMOS process, they enable tunability to integrated spiral inductors. In this paper, we present the design and simulations of a dual-band (2.4 GHz and 5 GHz) source degenerated low-noise amplifier (LNA) using memristive-via switched tunable inductors. Owing to the tunable inductor, the dual-band LNA has negligible area overhead compared to its single-band sibling. The LNA is designed using a 0.18-um RF CMOS technology and achieves a gain of 18.8 dB and 10.3 dB at 2.4 GHz and 5 GHz, respectively, and a noise figure (NF) below 2.3 dB at both bands. In addition, we present a semi-automated design methodology for the tunable inductors.

The growing demand for multi-band and multi-standard wireless devices requires flexible architectures that can reutilize the different blocks in the RF chains to reduce size and power consumption. The traditional multi-band radio with an RF chain per band is no longer scalable. Memristive devices have shown excellent performance as RF switches and also have a small footprint. Furthermore, as they are fabricated in the back-end of line of CMOS process, they enable tunability to integrated spiral inductors. In this paper, we present the design and simulations of a dual-band (2.4 GHz and 5 GHz) source degenerated low-noise amplifier (LNA) using memristive-via switched tunable inductors. Owing to the tunable inductor, the dual-band LNA has negligible area overhead compared to its single-band sibling. The LNA is designed using a 0.18-um RF CMOS technology and achieves a gain of 18.8 dB and 10.3 dB at 2.4 GHz and 5 GHz, respectively, and a noise figure (NF) below 2.3 dB at both bands. In addition, we present a semi-automated design methodology for the tunable inductors.

The growing demand for multi-band and multi-standard wireless devices requires flexible architectures that can reutilize the different blocks in the RF chains to reduce size and power consumption. The traditional multi-band radio with an RF chain per band is no longer scalable. Memristive devices have shown excellent performance as RF switches and also have a small footprint. Furthermore, as they are fabricated in the back-end of line of CMOS process, they enable tunability to integrated spiral inductors. In this paper, we present the design and simulations of a dual-band (2.4 GHz and 5 GHz) source degenerated low-noise amplifier (LNA) using memristive-via switched tunable inductors. Owing to the tunable inductor, the dual-band LNA has negligible area overhead compared to its single-band sibling. The LNA is designed using a 0.18-um RF CMOS technology and achieves a gain of 18.8 dB and 10.3 dB at 2.4 GHz and 5 GHz, respectively, and a noise figure (NF) below 2.3 dB at both bands. In addition, we present a semi-automated design methodology for the tunable inductors.

The growing demand for multi-band and multi-standard wireless devices requires flexible architectures that can reutilize the different blocks in the RF chains to reduce size and power consumption. The traditional multi-band radio with an RF chain per band is no longer scalable. Memristive devices have shown excellent performance as RF switches and also have a small footprint. Furthermore, as they are fabricated in the back-end of line of CMOS process, they enable tunability to integrated spiral inductors. In this paper, we present the design and simulations of a dual-band (2.4 GHz and 5 GHz) source degenerated low-noise amplifier (LNA) using memristive-via switched tunable inductors. Owing to the tunable inductor, the dual-band LNA has negligible area overhead compared to its single-band sibling. The LNA is designed using a 0.18-um RF CMOS technology and achieves a gain of 18.8 dB and 10.3 dB at 2.4 GHz and 5 GHz, respectively, and a noise figure (NF) below 2.3 dB at both bands. In addition, we present a semi-automated design methodology for the tunable inductors.

The growing demand for multi-band and multi-standard wireless devices requires flexible architectures that can reutilize the different blocks in the RF chains to reduce size and power consumption. The traditional multi-band radio with an RF chain per band is no longer scalable. Memristive devices have shown excellent performance as RF switches and also have a small footprint. Furthermore, as they are fabricated in the back-end of line of CMOS process, they enable tunability to integrated spiral inductors. In this paper, we present the design and simulations of a dual-band (2.4 GHz and 5 GHz) source degenerated low-noise amplifier (LNA) using memristive-via switched tunable inductors. Owing to the tunable inductor, the dual-band LNA has negligible area overhead compared to its single-band sibling. The LNA is designed using a 0.18-um RF CMOS technology and achieves a gain of 18.8 dB and 10.3 dB at 2.4 GHz and 5 GHz, respectively, and a noise figure (NF) below 2.3 dB at both bands. In addition, we present a semi-automated design methodology for the tunable inductors.

The growing demand for multi-band and multi-standard wireless devices requires flexible architectures that can reutilize the different blocks in the RF chains to reduce size and power consumption. The traditional multi-band radio with an RF chain per band is no longer scalable. Memristive devices have shown excellent performance as RF switches and also have a small footprint. Furthermore, as they are fabricated in the back-end of line of CMOS process, they enable tunability to integrated spiral inductors. In this paper, we present the design and simulations of a dual-band (2.4 GHz and 5 GHz) source degenerated low-noise amplifier (LNA) using memristive-via switched tunable inductors. Owing to the tunable inductor, the dual-band LNA has negligible area overhead compared to its single-band sibling. The LNA is designed using a 0.18-um RF CMOS technology and achieves a gain of 18.8 dB and 10.3 dB at 2.4 GHz and 5 GHz, respectively, and a noise figure (NF) below 2.3 dB at both bands. In addition, we present a semi-automated design methodology for the tunable inductors.

The growing demand for multi-band and multi-standard wireless devices requires flexible architectures that can reutilize the different blocks in the RF chains to reduce size and power consumption. The traditional multi-band radio with an RF chain per band is no longer scalable. Memristive devices have shown excellent performance as RF switches and also have a small footprint. Furthermore, as they are fabricated in the back-end of line of CMOS process, they enable tunability to integrated spiral inductors. In this paper, we present the design and simulations of a dual-band (2.4 GHz and 5 GHz) source degenerated low-noise amplifier (LNA) using memristive-via switched tunable inductors. Owing to the tunable inductor, the dual-band LNA has negligible area overhead compared to its single-band sibling. The LNA is designed using a 0.18-um RF CMOS technology and achieves a gain of 18.8 dB and 10.3 dB at 2.4 GHz and 5 GHz, respectively, and a noise figure (NF) below 2.3 dB at both bands. In addition, we present a semi-automated design methodology for the tunable inductors.

The growing demand for multi-band and multi-standard wireless devices requires flexible architectures that can reutilize the different blocks in the RF chains to reduce size and power consumption. The traditional multi-band radio with an RF chain per band is no longer scalable. Memristive devices have shown excellent performance as RF switches and also have a small footprint. Furthermore, as they are fabricated in the back-end of line of CMOS process, they enable tunability to integrated spiral inductors. In this paper, we present the design and simulations of a dual-band (2.4 GHz and 5 GHz) source degenerated low-noise amplifier (LNA) using memristive-via switched tunable inductors. Owing to the tunable inductor, the dual-band LNA has negligible area overhead compared to its single-band sibling. The LNA is designed using a 0.18-um RF CMOS technology and achieves a gain of 18.8 dB and 10.3 dB at 2.4 GHz and 5 GHz, respectively, and a noise figure (NF) below 2.3 dB at both bands. In addition, we present a semi-automated design methodology for the tunable inductors.

The growing demand for multi-band and multi-standard wireless devices requires flexible architectures that can reutilize the different blocks in the RF chains to reduce size and power consumption. The traditional multi-band radio with an RF chain per band is no longer scalable. Memristive devices have shown excellent performance as RF switches and also have a small footprint. Furthermore, as they are fabricated in the back-end of line of CMOS process, they enable tunability to integrated spiral inductors. In this paper, we present the design and simulations of a dual-band (2.4 GHz and 5 GHz) source degenerated low-noise amplifier (LNA) using memristive-via switched tunable inductors. Owing to the tunable inductor, the dual-band LNA has negligible area overhead compared to its single-band sibling. The LNA is designed using a 0.18-um RF CMOS technology and achieves a gain of 18.8 dB and 10.3 dB at 2.4 GHz and 5 GHz, respectively, and a noise figure (NF) below 2.3 dB at both bands. In addition, we present a semi-automated design methodology for the tunable inductors.

The growing demand for multi-band and multi-standard wireless devices requires flexible architectures that can reutilize the different blocks in the RF chains to reduce size and power consumption. The traditional multi-band radio with an RF chain per band is no longer scalable. Memristive devices have shown excellent performance as RF switches and also have a small footprint. Furthermore, as they are fabricated in the back-end of line of CMOS process, they enable tunability to integrated spiral inductors. In this paper, we present the design and simulations of a dual-band (2.4 GHz and 5 GHz) source degenerated low-noise amplifier (LNA) using memristive-via switched tunable inductors. Owing to the tunable inductor, the dual-band LNA has negligible area overhead compared to its single-band sibling. The LNA is designed using a 0.18-um RF CMOS technology and achieves a gain of 18.8 dB and 10.3 dB at 2.4 GHz and 5 GHz, respectively, and a noise figure (NF) below 2.3 dB at both bands. In addition, we present a semi-automated design methodology for the tunable inductors.

The growing demand for multi-band and multi-standard wireless devices requires flexible architectures that can reutilize the different blocks in the RF chains to reduce size and power consumption. The traditional multi-band radio with an RF chain per band is no longer scalable. Memristive devices have shown excellent performance as RF switches and also have a small footprint. Furthermore, as they are fabricated in the back-end of line of CMOS process, they enable tunability to integrated spiral inductors. In this paper, we present the design and simulations of a dual-band (2.4 GHz and 5 GHz) source degenerated low-noise amplifier (LNA) using memristive-via switched tunable inductors. Owing to the tunable inductor, the dual-band LNA has negligible area overhead compared to its single-band sibling. The LNA is designed using a 0.18-um RF CMOS technology and achieves a gain of 18.8 dB and 10.3 dB at 2.4 GHz and 5 GHz, respectively, and a noise figure (NF) below 2.3 dB at both bands. In addition, we present a semi-automated design methodology for the tunable inductors.

The growing demand for multi-band and multi-standard wireless devices requires flexible architectures that can reutilize the different blocks in the RF chains to reduce size and power consumption. The traditional multi-band radio with an RF chain per band is no longer scalable. Memristive devices have shown excellent performance as RF switches and also have a small footprint. Furthermore, as they are fabricated in the back-end of line of CMOS process, they enable tunability to integrated spiral inductors. In this paper, we present the design and simulations of a dual-band (2.4 GHz and 5 GHz) source degenerated low-noise amplifier (LNA) using memristive-via switched tunable inductors. Owing to the tunable inductor, the dual-band LNA has negligible area overhead compared to its single-band sibling. The LNA is designed using a 0.18-um RF CMOS technology and achieves a gain of 18.8 dB and 10.3 dB at 2.4 GHz and 5 GHz, respectively, and a noise figure (NF) below 2.3 dB at both bands. In addition, we present a semi-automated design methodology for the tunable inductors.

The growing demand for multi-band and multi-standard wireless devices requires flexible architectures that can reutilize the different blocks in the RF chains to reduce size and power consumption. The traditional multi-band radio with an RF chain per band is no longer scalable. Memristive devices have shown excellent performance as RF switches and also have a small footprint. Furthermore, as they are fabricated in the back-end of line of CMOS process, they enable tunability to integrated spiral inductors. In this paper, we present the design and simulations of a dual-band (2.4 GHz and 5 GHz) source degenerated low-noise amplifier (LNA) using memristive-via switched tunable inductors. Owing to the tunable inductor, the dual-band LNA has negligible area overhead compared to its single-band sibling. The LNA is designed using a 0.18-um RF CMOS technology and achieves a gain of 18.8 dB and 10.3 dB at 2.4 GHz and 5 GHz, respectively, and a noise figure (NF) below 2.3 dB at both bands. In addition, we present a semi-automated design methodology for the tunable inductors.

The growing demand for multi-band and multi-standard wireless devices requires flexible architectures that can reutilize the different blocks in the RF chains to reduce size and power consumption. The traditional multi-band radio with an RF chain per band is no longer scalable. Memristive devices have shown excellent performance as RF switches and also have a small footprint. Furthermore, as they are fabricated in the back-end of line of CMOS process, they enable tunability to integrated spiral inductors. In this paper, we present the design and simulations of a dual-band (2.4 GHz and 5 GHz) source degenerated low-noise amplifier (LNA) using memristive-via switched tunable inductors. Owing to the tunable inductor, the dual-band LNA has negligible area overhead compared to its single-band sibling. The LNA is designed using a 0.18-um RF CMOS technology and achieves a gain of 18.8 dB and 10.3 dB at 2.4 GHz and 5 GHz, respectively, and a noise figure (NF) below 2.3 dB at both bands. In addition, we present a semi-automated design methodology for the tunable inductors.

The growing demand for multi-band and multi-standard wireless devices requires flexible architectures that can reutilize the different blocks in the RF chains to reduce size and power consumption. The traditional multi-band radio with an RF chain per band is no longer scalable. Memristive devices have shown excellent performance as RF switches and also have a small footprint. Furthermore, as they are fabricated in the back-end of line of CMOS process, they enable tunability to integrated spiral inductors. In this paper, we present the design and simulations of a dual-band (2.4 GHz and 5 GHz) source degenerated low-noise amplifier (LNA) using memristive-via switched tunable inductors. Owing to the tunable inductor, the dual-band LNA has negligible area overhead compared to its single-band sibling. The LNA is designed using a 0.18-um RF CMOS technology and achieves a gain of 18.8 dB and 10.3 dB at 2.4 GHz and 5 GHz, respectively, and a noise figure (NF) below 2.3 dB at both bands. In addition, we present a semi-automated design methodology for the tunable inductors.

The growing demand for multi-band and multi-standard wireless devices requires flexible architectures that can reutilize the different blocks in the RF chains to reduce size and power consumption. The traditional multi-band radio with an RF chain per band is no longer scalable. Memristive devices have shown excellent performance as RF switches and also have a small footprint. Furthermore, as they are fabricated in the back-end of line of CMOS process, they enable tunability to integrated spiral inductors. In this paper, we present the design and simulations of a dual-band (2.4 GHz and 5 GHz) source degenerated low-noise amplifier (LNA) using memristive-via switched tunable inductors. Owing to the tunable inductor, the dual-band LNA has negligible area overhead compared to its single-band sibling. The LNA is designed using a 0.18-um RF CMOS technology and achieves a gain of 18.8 dB and 10.3 dB at 2.4 GHz and 5 GHz, respectively, and a noise figure (NF) below 2.3 dB at both bands. In addition, we present a semi-automated design methodology for the tunable inductors.

The growing demand for multi-band and multi-standard wireless devices requires flexible architectures that can reutilize the different blocks in the RF chains to reduce size and power consumption. The traditional multi-band radio with an RF chain per band is no longer scalable. Memristive devices have shown excellent performance as RF switches and also have a small footprint. Furthermore, as they are fabricated in the back-end of line of CMOS process, they enable tunability to integrated spiral inductors. In this paper, we present the design and simulations of a dual-band (2.4 GHz and 5 GHz) source degenerated low-noise amplifier (LNA) using memristive-via switched tunable inductors. Owing to the tunable inductor, the dual-band LNA has negligible area overhead compared to its single-band sibling. The LNA is designed using a 0.18-um RF CMOS technology and achieves a gain of 18.8 dB and 10.3 dB at 2.4 GHz and 5 GHz, respectively, and a noise figure (NF) below 2.3 dB at both bands. In addition, we present a semi-automated design methodology for the tunable inductors.

The growing demand for multi-band and multi-standard wireless devices requires flexible architectures that can reutilize the different blocks in the RF chains to reduce size and power consumption. The traditional multi-band radio with an RF chain per band is no longer scalable. Memristive devices have shown excellent performance as RF switches and also have a small footprint. Furthermore, as they are fabricated in the back-end of line of CMOS process, they enable tunability to integrated spiral inductors. In this paper, we present the design and simulations of a dual-band (2.4 GHz and 5 GHz) source degenerated low-noise amplifier (LNA) using memristive-via switched tunable inductors. Owing to the tunable inductor, the dual-band LNA has negligible area overhead compared to its single-band sibling. The LNA is designed using a 0.18-um RF CMOS technology and achieves a gain of 18.8 dB and 10.3 dB at 2.4 GHz and 5 GHz, respectively, and a noise figure (NF) below 2.3 dB at both bands. In addition, we present a semi-automated design methodology for the tunable inductors.

The growing demand for multi-band and multi-standard wireless devices requires flexible architectures that can reutilize the different blocks in the RF chains to reduce size and power consumption. The traditional multi-band radio with an RF chain per band is no longer scalable. Memristive devices have shown excellent performance as RF switches and also have a small footprint. Furthermore, as they are fabricated in the back-end of line of CMOS process, they enable tunability to integrated spiral inductors. In this paper, we present the design and simulations of a dual-band (2.4 GHz and 5 GHz) source degenerated low-noise amplifier (LNA) using memristive-via switched tunable inductors. Owing to the tunable inductor, the dual-band LNA has negligible area overhead compared to its single-band sibling. The LNA is designed using a 0.18-um RF CMOS technology and achieves a gain of 18.8 dB and 10.3 dB at 2.4 GHz and 5 GHz, respectively, and a noise figure (NF) below 2.3 dB at both bands. In addition, we present a semi-automated design methodology for the tunable inductors.

The growing demand for multi-band and multi-standard wireless devices requires flexible architectures that can reutilize the different blocks in the RF chains to reduce size and power consumption. The traditional multi-band radio with an RF chain per band is no longer scalable. Memristive devices have shown excellent performance as RF switches and also have a small footprint. Furthermore, as they are fabricated in the back-end of line of CMOS process, they enable tunability to integrated spiral inductors. In this paper, we present the design and simulations of a dual-band (2.4 GHz and 5 GHz) source degenerated low-noise amplifier (LNA) using memristive-via switched tunable inductors. Owing to the tunable inductor, the dual-band LNA has negligible area overhead compared to its single-band sibling. The LNA is designed using a 0.18-um RF CMOS technology and achieves a gain of 18.8 dB and 10.3 dB at 2.4 GHz and 5 GHz, respectively, and a noise figure (NF) below 2.3 dB at both bands. In addition, we present a semi-automated design methodology for the tunable inductors.

The growing demand for multi-band and multi-standard wireless devices requires flexible architectures that can reutilize the different blocks in the RF chains to reduce size and power consumption. The traditional multi-band radio with an RF chain per band is no longer scalable. Memristive devices have shown excellent performance as RF switches and also have a small footprint. Furthermore, as they are fabricated in the back-end of line of CMOS process, they enable tunability to integrated spiral inductors. In this paper, we present the design and simulations of a dual-band (2.4 GHz and 5 GHz) source degenerated low-noise amplifier (LNA) using memristive-via switched tunable inductors. Owing to the tunable inductor, the dual-band LNA has negligible area overhead compared to its single-band sibling. The LNA is designed using a 0.18-um RF CMOS technology and achieves a gain of 18.8 dB and 10.3 dB at 2.4 GHz and 5 GHz, respectively, and a noise figure (NF) below 2.3 dB at both bands. In addition, we present a semi-automated design methodology for the tunable inductors.

The growing demand for multi-band and multi-standard wireless devices requires flexible architectures that can reutilize the different blocks in the RF chains to reduce size and power consumption. The traditional multi-band radio with an RF chain per band is no longer scalable. Memristive devices have shown excellent performance as RF switches and also have a small footprint. Furthermore, as they are fabricated in the back-end of line of CMOS process, they enable tunability to integrated spiral inductors. In this paper, we present the design and simulations of a dual-band (2.4 GHz and 5 GHz) source degenerated low-noise amplifier (LNA) using memristive-via switched tunable inductors. Owing to the tunable inductor, the dual-band LNA has negligible area overhead compared to its single-band sibling. The LNA is designed using a 0.18-um RF CMOS technology and achieves a gain of 18.8 dB and 10.3 dB at 2.4 GHz and 5 GHz, respectively, and a noise figure (NF) below 2.3 dB at both bands. In addition, we present a semi-automated design methodology for the tunable inductors.

The growing demand for multi-band and multi-standard wireless devices requires flexible architectures that can reutilize the different blocks in the RF chains to reduce size and power consumption. The traditional multi-band radio with an RF chain per band is no longer scalable. Memristive devices have shown excellent performance as RF switches and also have a small footprint. Furthermore, as they are fabricated in the back-end of line of CMOS process, they enable tunability to integrated spiral inductors. In this paper, we present the design and simulations of a dual-band (2.4 GHz and 5 GHz) source degenerated low-noise amplifier (LNA) using memristive-via switched tunable inductors. Owing to the tunable inductor, the dual-band LNA has negligible area overhead compared to its single-band sibling. The LNA is designed using a 0.18-um RF CMOS technology and achieves a gain of 18.8 dB and 10.3 dB at 2.4 GHz and 5 GHz, respectively, and a noise figure (NF) below 2.3 dB at both bands. In addition, we present a semi-automated design methodology for the tunable inductors.

The growing demand for multi-band and multi-standard wireless devices requires flexible architectures that can reutilize the different blocks in the RF chains to reduce size and power consumption. The traditional multi-band radio with an RF chain per band is no longer scalable. Memristive devices have shown excellent performance as RF switches and also have a small footprint. Furthermore, as they are fabricated in the back-end of line of CMOS process, they enable tunability to integrated spiral inductors. In this paper, we present the design and simulations of a dual-band (2.4 GHz and 5 GHz) source degenerated low-noise amplifier (LNA) using memristive-via switched tunable inductors. Owing to the tunable inductor, the dual-band LNA has negligible area overhead compared to its single-band sibling. The LNA is designed using a 0.18-um RF CMOS technology and achieves a gain of 18.8 dB and 10.3 dB at 2.4 GHz and 5 GHz, respectively, and a noise figure (NF) below 2.3 dB at both bands. In addition, we present a semi-automated design methodology for the tunable inductors.