In recent years, convolutional neural networks (CNNs) have shown great performance in
various fields such as image classification, pattern recognition, and multi-media …

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 …

Deep neural networks with applications from computer vision to medical diagnosis,,,–are
commonly implemented using clock-based processors,,,,,,,–, in which computation speed is …

Abstract Deep Convolutional Neural Network (CNN) is a special type of Neural Networks,
which has shown exemplary performance on several competitions related to Computer …

This paper presents an energy-efficient static random access memory (SRAM) with
embedded dot-product computation capability, for binary-weight convolutional neural …

The advent of dedicated Deep Learning (DL) accelerators and neuromorphic processors
has brought on new opportunities for applying both Deep and Spiking Neural Network …

Computation-in-memory (CIM) is a promising candidate to improve the energy efficiency of
multiply-and-accumulate (MAC) operations of artificial intelligence (AI) chips. This work …

This article (Colonnade) presents a fully digital bit-serial compute-in-memory (CIM) macro.
The digital CIM macro is designed for processing neural networks with reconfigurable 1-16 …

This article presents a computing-in-memory (CIM) structure aimed at improving the energy
efficiency of edge devices running multi-bit multiply-and-accumulate (MAC) operations. The …

In the past few years, the demand for real-time hardware implementations of deep neural
networks (DNNs), especially convolutional neural networks (CNNs), has dramatically …