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 …

Computing in memory (CIM) could be used to overcome the von Neumann bottleneck and to
provide sustainable improvements in computing throughput and energy efficiency …

Abstract Models of artificial intelligence (AI) that have billions of parameters can achieve
high accuracy across a range of tasks,, but they exacerbate the poor energy efficiency of …

From the cloud to edge devices, artificial intelligence (AI) and machine learning (ML) are
widely used in many cognitive tasks, such as image classification and speech recognition. In …

When applied to artificial intelligence edge devices, the conventionally von Neumann
computing architecture imposes numerous challenges (eg, improving the energy efficiency) …

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 …

Battery-powered tiny-AI edge devices require large-capacity nonvolatile compute-in-memory
(nvCIM), with multibit input (IN), weight (W), and output (OUT) precision to support complex …

Computing-in-memory (CIM) is being widely explored to minimize power consumption in
data movement and multiply-and-accumulate (MAC) for edge-AI devices. Although most …

Recent SRAM-based computation-in-memory (CIM) macros enable mid-to-high precision
multiply-and-accumulate (MAC) operations with improved energy efficiency using ultra …

SRAM-based computing in memory (SRAM-CIM) is an attractive approach to improve the
energy efficiency (EF) of edge-AI devices performing multiply-and-accumulate (MAC) …