Traditional von Neumann computing systems involve separate processing and memory
units. However, data movement is costly in terms of time and energy and this problem is …

Domain-specific hardware is becoming a promising topic in the backdrop of improvement
slow down for general-purpose processors due to the foreseeable end of Moore's Law …

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 …

Artificial Intelligence (AI) is currently experiencing a bloom driven by deep learning (DL)
techniques, which rely on networks of connected simple computing units operating in …

Currently, Machine Learning (ML) is becoming ubiquitous in everyday life. Deep Learning
(DL) is already present in many applications ranging from computer vision for medicine to …

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 …

Analog hardware accelerators, which perform computation within a dense memory array,
have the potential to overcome the major bottlenecks faced by digital hardware for data …

Large-scale matrix-vector multiplications, which dominate in deep neural networks (DNNs),
are limited by data movement in modern VLSI technologies. This paper addresses data …

This paper presents a scalable neural-network (NN) inference accelerator in 16nm, based
on an array of programmable cores employing mixed-signal In-Memory Computing (IMC) …