A stochastic input encoding scheme (CODEX) is presented that aims to relax the analog-to-
digital converter (ADC) design requirements in memristor crossbar systems. CODEX …

Acceleration of training and inference of Deep Neural Networks (DNNs) with non-volatile
memory (NVM) arrays, such as Phase-Change Memory (PCM), shows promising …

On-chip learning in spin orbit torque driven domain wall synapse based crossbar fully
connected neural network (FCNN) has been shown to be extremely efficient in terms of …

Analog Non-Volatile Memory-based accelerators offer high-throughput and energy-efficient
Multiply-Accumulate operations for the large Fully-Connected layers that dominate …

With advancements in machine learning technology, networks are becoming increasingly
complex, and the extent of the computation involved is increasing. Consequently, the …

Analog hardware Neural Network (NN) that uses a crossbar array of synapses to store the
weights of the NN provides an extremely fast and energy efficient hardware platform to …

A fully analogue implementation of training algorithms would speed up the training of
artificial neural networks. A common choice for training the feedforward networks is the …

With the increasing demands in modern communications, the concepts of energy-efficient
and low-cost processors have received a lot of attention from researchers in recent years …

This paper presents an innovative concept of a high‐speed and higher order adaptive filter.
A fully analog artificial neural network handles the adaption by using a filter bank for filtering …

In this work, we proposed a three-dimensional (3-D) channel stacked array architecture
based on charge-trap flash (CTF) memory for an artificial neural network accelerator. The …