In recent years, the limits of the multicore approach emerged in the so-called “dark silicon”
issue and diminishing returns of an ever-increasing core count. Hardware manufacturers …

Processing-in-memory (PIM) is a promising architecture to design various types of neural
network accelerators as it ensures the efficiency of computation together with Resistive …

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 …

Processing-In-Memory (PIM) accelerators have the potential to efficiently run Deep Neural
Network (DNN) inference by reducing costly data movement and by using resistive RAM …

Graph convolutional networks (GCNs) are promising to enable machine learning on graphs.
GCNs exhibit mixed computational kernels, involving regular neural-network-like computing …

Resistive-random-access-memory (ReRAM) based processing-in-memory (R2PIM)
accelerators show promise in bridging the gap between Internet of Thing devices' …

The amalgamation of artificial intelligence with Internet of Things (AIoT) devices have seen a
rapid surge in growth, largely due to the effective implementation of deep neural network …

Specialized accelerators have recently garnered attention as a method to reduce the power
consumption of neural network inference. A promising category of accelerators utilizes …

To combat the high energy costs of moving data between main memory and the CPU, recent
works have proposed to perform processing-using-memory (PUM), a type of processing-in …

Compute-In-Memory (CiM) is a promising solution to accelerate Deep Neural Networks
(DNNs) as it can avoid energy-intensive DNN weight movement and use memory arrays to …