Deep learning has become ubiquitous, touching daily lives across the globe. Today,
traditional computer architectures are stressed to their limits in efficiently executing the …

Memory technologies with higher density, higher bandwidth, lower power consumption,
higher speed, and lower cost are in high demand in the current big data era. In this paper …

Hardware architectures composed of resistive cross-point device arrays can provide
significant power and speed benefits for deep neural network training workloads using …

Neural networks (NNs) are efficient tools for rapidly obtaining geoelectric models to solve
magnetotelluric (MT) inversion problems. Training an NN with strong predictive power …

ABSTRACT Analog In-Memory Computing (AIMC) is a promising approach to reduce the
latency and energy consumption of Deep Neural Network (DNN) inference and training …

Analog in-memory computing is a promising future technology for efficiently accelerating
deep learning networks. While using in-memory computing to accelerate the inference …

Analog crossbar arrays comprising programmable non-volatile resistors are under intense
investigation for acceleration of deep neural network training. However, the ubiquitous …

Recent progress in novel non-volatile memory-based synaptic device technologies and their
feasibility for matrix-vector multiplication (MVM) has ignited active research on implementing …

Deep neural networks (DNNs) are typically trained using the conventional stochastic
gradient descent (SGD) algorithm. However, SGD performs poorly when applied to train …

We present the fabrication of 4 K-scale electrochemical random-access memory (ECRAM)
cross-point arrays for analog neural network training accelerator and an electrical …