Compute-in-memory (CIM) is a new computing paradigm that addresses the memory-wall
problem in hardware accelerator design for deep learning. The input vector and weight …

Successful integration of deep neural networks (DNNs) or deep learning (DL) has resulted
in breakthroughs in many areas. However, deploying these highly accurate models for data …

A recent trend in deep neural network (DNN) development is to extend the reach of deep
learning applications to platforms that are more resource and energy-constrained, eg …

We present XNOR-SRAM, a mixed-signal in-memory computing (IMC) SRAM macro that
computes ternary-XNOR-and-accumulate (XAC) operations in binary/ternary deep neural …

This comprehensive review summarizes state of the art, challenges, and prospects of the
neuro-inspired computing with emerging nonvolatile memory devices. First, we discuss the …

This book provides a structured treatment of the key principles and techniques for enabling
efficient processing of deep neural networks (DNNs). DNNs are currently widely used for …

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

This article presents C3SRAM, an in-memory-computing SRAM macro. The macro is an
SRAM module with the circuits embedded in bitcells and peripherals to perform hardware …

An energy-efficient deep neural network (DNN) accelerator, unified neural processing unit
(UNPU), is proposed for mobile deep learning applications. The UNPU can support both …

The low communication bandwidth between memory and processing units in conventional
von Neumann machines does not support the requirements of emerging applications that …