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 …

From the discovery of giant magnetoresistance (GMR) to tunnel magnetoresistance (TMR),
their subsequent application in large capacity hard disk drives (HDDs) greatly speeded up …

Modern computing systems are overwhelmingly designed to move data to computation. This
design choice goes directly against at least three key trends in computing that cause …

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

This paper presents the Neural Cache architecture, which re-purposes cache structures to
transform them into massively parallel compute units capable of running inferences for Deep …

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 …

Many modern workloads, such as neural networks, databases, and graph processing, are
fundamentally memory-bound. For such workloads, the data movement between main …

We are experiencing an explosive growth in the number of consumer devices, including
smartphones, tablets, web-based computers such as Chromebooks, and wearable devices …

After a plethora of high-profile RowHammer attacks, CPU and DRAM vendors scrambled to
deliver what was meant to be the definitive hardware solution against the RowHammer …