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Loai Danial’s paper has been accepted to a special issue in IEEE JETCAS!

December 6th, 2017

 “DIDACTIC: A Data-Intelligent Digital-to-Analog Converter with a Trainable Integrated Circuit using Memristors”, by Loai Danial, Nicolas Wainstein, Shraga Kraus and Shahar Kvatinsky, was accepted to a special issue on Low-Power Adaptive Neuromorphic Systems, Devices, Circuit, Architectures, and Algorithms, in IEEE Journal on Emerging and Selected Topics in Circuits and Systems.


In an increasingly data-diverse world, in which data
are interactively transferred at high rates, there is an ever-growing
demand for high precision data converters. In this paper, we
propose a novel digital-to-analog converter (DAC) configuration
that is calibrated using an artificial intelligence neural network
technique. The proposed technique is demonstrated on an
adaptive and self-calibrated binary-weighted DAC that can be
configured on-chip in real time. We design a reconfigurable fourbit
DAC with a memristor-based neural network. This circuit uses
an online supervised machine learning algorithm called “binary-weighted
time-varying gradient descent.” This algorithm fits
multiple full-scale voltage ranges and sampling frequencies by
iterative synaptic adjustments, while inherently providing
mismatch calibration and noise tolerance. Theoretical analysis, as
well as simulation results, show the efficiency and robustness of
the training algorithm in reconfiguration, self-calibration, and
desensitization, leading to a significant improvement in DAC
accuracy: 0.12 LSB in terms of integral non-linearity (INL), 0.11
LSB in terms of differential non-linearity (DNL), and 3.63 bits in
terms of an effective number of bits (ENOB). The findings constitute
a promising milestone towards scalable data-driven converters
using deep neural networks.
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