As von Neumann computing architectures become increasingly constrained by data-
movement overheads, researchers have started exploring in-memory computing (IMC) …

This paper presents an extensive literature review on Binary Neural Network (BNN). BNN
utilizes binary weights and activation function parameters to substitute the full-precision …

Training machine learning (ML) algorithms is a computationally intensive process, which is
frequently memory-bound due to repeatedly accessing large training datasets. As a result …

Rapid development in deep neural networks (DNNs) is enabling many intelligent
applications. However, on-chip training of DNNs is challenging due to the extensive …

Compute-in-memory (CIM) with emerging non-volatile memories (eNVMs) is time and
energy efficient for deep neural network (DNN) inference. However, challenges still remain …

On-chip training of large-scale deep neural networks (DNNs) is challenging due to
computational complexity and resource limitation. Compute-in-memory (CIM) architecture …

Artificial intelligence (AI) processes data-centric applications with minimal effort. However, it
poses new challenges to system design in terms of computational speed and energy …

Training machine learning (ML) algorithms is a computationally intensive process, which is
frequently memory-bound due to repeatedly accessing large training datasets. As a result …

Unary computing, whose arithmetics require only one logic gate, has enabled efficient DNN
processing, especially on strictly power-constrained devices. However, unary computing still …

Modern Machine Learning (ML) training on large-scale datasets is a very time-consuming
workload. It relies on the optimization algorithm Stochastic Gradient Descent (SGD) due to …