Undergraduate Projects

Other

Characterization of Novel Electrical Coating PUF Dies for Cyber Security
Project taken
Supervisor(s):
Description:
Requirements:
Electrical Engineering Lab 1+2
...

Traditional approaches to storing secret keys in memory for cryptographic applications are

susceptible to physical attacks when an attacker gains access to the storage medium.

To address this issue, physically unclonable functions (PUFs) have emerged as a novel concept for

secure key storage. PUFs store the cryptographic key as unique analog identifiers within the hardware, rather than in

memory elements, making them resilient against physical attacks.

 

In this research, we propose the study of novel coating-based PUFs that leverage nanoscale

inhomogeneity, randomness, and uniqueness in nanomaterials and nanostructures.

Our study encompasses the design, simulations, fabrication, and characterization of physically

unclonable coatings, along with the development of metrics to evaluate their effectiveness.

In-Memory Training Of Ternary NN (TNN)
Project available
Supervisor(s):
Description:
Requirements:
Courses: Machine Learning * Programming: Python
In-Memory Training Of Ternary NN (TNN)

Recently, several different memristive technologies (ReRAM, CBRAM, PCM and STT-MRAM) have

emerged as promising candidates for digital and analog in-memory computation.

Deep neural networks (DNNs) are one of the main applications to benefit from analog in-memory

computation.

In this project you will simulate in-memory training of ternary neural networks (TNNs) with a magnetic

tunnel junction (MTJ) device.

Reservoir Computing for Static and Temporal Tasks
Project available
Supervisor(s):
Description:
Requirements:
Tools: Matlab/Python, SPICE Prerequisite * Courses: Machine Learning, Introduction to VLSI is a plus
Reservoir Computing for Static and Temporal Tasks

Reservoir computing is a kind of neural network architecture that is derived from recurrent neural

networks. It consists of a dynamic and non-linear reservoir module that maps the input data into high

dimensional space. This is then read-out using a fully connected readout layer. The advantage of this

network is that only the readout layer requires training, usually by regression.

Current Compliance Implementation
Project taken
Supervisor(s):
Description:
Requirements:
Electrical Engineering Lab 1+2
Current Compliance Implementation

Emerging memristors are novel circuit elements, originally described as the “fourth missing circuit

element” and considered today as the future of nonvolatile memory. Different memristors have been

developed and simulatively characterized by the Technion’s ASIC² research group, headed by Prof.

Shahar Kvatinsky. Current Compliance (CC) is a factor that can significantly influence the memristive devices

performance.

State-of-the-art apparatus propose CC circuit with response time of 100𝜇Sec to 1mSec, this slow

response time may cause undesired behavior of the memristive devices or even damage the device.

Our target is to potentially improve the measurement setup of research memristive devices by

designing a small CC circuit.