Deep neural networks offer considerable potential across a range of applications, from
advanced manufacturing to autonomous cars. A clear trend in deep neural networks is the …

Hardware realization of artificial intelligence (AI) requires new design styles and even
underlying technologies than those used in traditional digital processors or logic circuits …

This paper presents an energy-efficient static random access memory (SRAM) with
embedded dot-product computation capability, for binary-weight convolutional neural …

An energy-efficient deep neural network (DNN) accelerator, unified neural processing unit
(UNPU), is proposed for mobile deep learning applications. The UNPU can support both …

This article presents C3SRAM, an in-memory-computing SRAM macro. The macro is an
SRAM module with the circuits embedded in bitcells and peripherals to perform hardware …

Shifting computing architectures from von Neumann to event-based spiking neural networks
(SNNs) uncovers new opportunities for low-power processing of sensory data in …

As the use of deep neural networks continues to grow, so does the fraction of compute
cycles devoted to their execution. This has led the CAD and architecture communities to …

Neuromorphic computing is henceforth a major research field for both academic and
industrial actors. As opposed to Von Neumann machines, brain-inspired processors aim at …

Large-scale deep neural networks (DNNs) are both compute and memory intensive. As the
size of DNNs continues to grow, it is critical to improve the energy efficiency and …

Recent studies from several hyperscalars pinpoint to embedding layers as the most memory-
intensive deep learning (DL) algorithm being deployed in today's datacenters. This paper …