Scientific workloads have traditionally exploited high levels of sparsity to accelerate
computation and reduce memory requirements. While deep neural networks can be made …

The symmetric sparse matrix-vector multiplication (SymmSpMV) is an important building
block for many numerical linear algebra kernel operations or graph traversal applications …

Tensor factorizations are powerful tools in many machine learning and data analytics
applications. Tensors are often sparse, which makes sparse tensor factorizations memory …

The success of DNN pruning has led to the development of energy-efficient inference
accelerators that support pruned models with sparse weight and activation tensors. Because …

Due to the limited capacity of GPU memory, the majority of prior work on graph applications
on GPUs has been restricted to graphs of modest sizes that fit in memory. Recent hardware …

With the introduction of SNIP [arXiv: 1810.02340 v2], it has been demonstrated that modern
neural networks can effectively be pruned before training. Yet, its sensitivity criterion has …

This paper shows how to generate code that efficiently converts sparse tensors between
disparate storage formats (data layouts) such as CSR, DIA, ELL, and many others. We …

The GraphBLAS is a programming model that expresses graph algorithms in linear
algebraic terms. It takes an easy-to-use, data-centric view where algebraic operations …

Graph pattern mining applications try to find all embeddings that match specific patterns.
Compared to the traditional graph computation, graph mining applications are computation …

We present the first application of field programmable gate arrays (FPGAs) as new,
customizable hardware architectures for carrying out fast and energy-efficient quantum …