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 …

Modern deep neural networks (DNNs) require billions of multiply-accumulate operations per
inference. Given that these computations demand relatively low precision, it is feasible to …

Abstract Machine learning algorithms have been successfully deployed in cloud-centric
applications and on computationally powerful digital platforms such as high-end FPGAs and …

Modern deep neural networks require energy-and area-efficient multi-bit multiply-
accumulate (MAC) functions. In-memory computing (IMC) with analog accumulation has …

Resistive random-access memory (RRAM) based compute-in-memory (CIM) is an emerging
approach to address the demand for practical implementation of artificial intelligence (AI) on …

A read-only memory (ROM) computing unit utilized in matrix operations of a neural network
comprising a unit element including one or more connections, wherein a weight associated …

Computation in Memory (CIM) is a groundbreaking concept that involves performing
computations directly within the memory itself, eliminating the need to transfer data back and …

This work presents a novel approach in Digital to Pulse Converter (DPC) design, which is
used as an alternative to Digital to Analog Converter (DAC) for Analog in Memory Compute …

Deep neural networks have emerged as important new drivers for custom VLSI computing
hardware in resource-constrained environments. In this context, this chapter reviews …

Performing ever-demanding artificial intelligence (AI) tasks directly on resource-constrained
edge devices calls for unprecedented energy-efficiency of edge AI hardware. AI hardware …