A number of recent efforts have attempted to design accelerators for popular machine
learning algorithms, such as those involving convolutional and deep neural networks (CNNs …

Many recent works take advantage of highly parallel analog in-situ computation in memristor
crossbars to accelerate the many vector-matrix multiplication operations in deep neural …

Memristor crossbars are circuits capable of performing analog matrix-vector multiplications,
overcoming the fundamental energy efficiency limitations of digital logic. They have been …

Vector-matrix multiplication dominates the computation time and energy for many workloads,
particularly neural network algorithms and linear transforms (eg, the Discrete Fourier …

Deep neural networks (DNNs) have attracted substantial interest in recent years due to their
superior performance on many classification and regression tasks as compared to other …

Memristors are now becoming a prominent candidate to serve as the building blocks of non-
von Neumann in-memory computing architectures. By mapping analog numerical matrices …

To enable essential deep learning computation on energy-constrained hardware platforms,
including mobile, wearable, and Internet of Things (IoT) devices, a number of digital ASIC …

Artificial neural networks have become ubiquitous in modern life, which has triggered the
emergence of a new class of application specific integrated circuits for their acceleration …

Using memristor crossbar arrays to accelerate computations is a promising approach to
efficiently implement algorithms in deep neural networks. Early demonstrations, however …

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 …