Sparse matrix-vector multiplication (SpMV) plays a key role in computational science and
engineering, graph processing, and machine learning applications. Much work on SpMV …

Thermodynamic computing exploits fluctuations and dissipation in physical systems to
efficiently solve various mathematical problems. It was recently shown that certain linear …

Machine Learning (ML) workloads generally contain a significant amount of matrix
computations; hence, hardware accelerators for ML have been incorporating support for …

Analysis of multi-dimensional data, especially tensor decomposition, which extracts latent
information, is becoming considerably popular. Although multi-dimensional sparse data is …

Abstract Tensor Core Units (TCUs) are hardware accelerators developed for deep neural
networks, which efficiently support the multiplication of two dense m * m matrices, where m is …

Tensor accelerators have gained popularity because they provide a cheap and efficient
solution for speeding up computational-expensive tasks in Deep Learning and, more …

Set similarity join (SSJ) is a well studied problem with many algorithms proposed to speed
up its performance. However, its scalability and performance are rarely discussed in modern …

Tensor cores in the recent NVIDIA GPUs are under the spotlight due to their superior
computation throughput for general matrix-matrix multiplication (GEMM) that has been …

This paper studies how to accelerate a single channel 2D image convolution using NVIDIA's
Tensor Core. Tensor Core is a dedicated arithmetic unit for speeding up matrix products and …

We present a parallel scan (prefix sum) algorithm in the Tensor Core Unit (TCU) model of
computation. The TCU model assumes that multiplication between two square matrices of …