Deep learning has revolutionized the fields of computer vision, natural language
understanding, speech recognition, information retrieval, and more. However, with the …

In this article, we provide a comprehensive introduction to knowledge graphs, which have
recently garnered significant attention from both industry and academia in scenarios that …

It has long been known that photonic communication can alleviate the data movement
bottlenecks that plague conventional microelectronic processors. More recently, there has …

Training deep learning models is compute-intensive and there is an industry-wide trend
towards hardware specialization to improve performance. To systematically benchmark …

Generalized Sparse Matrix-Matrix Multiplication (SpGEMM) is a ubiquitous task in various
engineering and scientific applications. However, inner product based SpGEMM introduces …

Training of large-scale deep neural networks is often constrained by the available
computational resources. We study the effect of limited precision data representation and …

The advent of dedicated Deep Learning (DL) accelerators and neuromorphic processors
has brought on new opportunities for applying both Deep and Spiking Neural Network …

Genome sequence analysis has enabled significant advancements in medical and scientific
areas such as personalized medicine, outbreak tracing, and the understanding of evolution …

We present a 256 256 in-memory compute (IMC) core designed and fabricated in 14-nm
CMOS technology with backend-integrated multi-level phase change memory (PCM). It …

This paper describes a novel approach of packing sparse convolutional neural networks into
a denser format for efficient implementations using systolic arrays. By combining multiple …