With the increasing size of DNN models and the growing discrepancy between compute
performance and memory bandwidth, fusing multiple layers together to reduce off-chip …

The widespread use of Sparse Matrix Dense Matrix Multiplication (SpMM) and Sampled
Dense Matrix Dense Matrix Multiplication (SDDMM) kernels makes them candidates for …

Solving sparse systems of linear equations is a crucial component in many science and
engineering problems, like simulating physical systems. Sparse matrix factorization …

Accelerating matrix multiplication is crucial to achieve high performance in many application
domains, including neural networks, graph analytics, and scientific computing. These …

Spiking Neural Networks (SNNs) have gained significant research attention over the past
decade due to their potential for enabling resource-constrained edge devices. While existing …

Recently, sparse tensor algebra (SpTA) plays an increasingly important role in machine
learning. However, due to the unstructured sparsity of SpTA, the general-purpose …

Sparse-dense matrix multiplication (SpMM) is the performance bottleneck of many high-
performance and deep-learning applications, making it attractive to design specialized …

Sparse linear algebra (SLA) operations are fundamental building blocks for many important
applications such as data analytics, graph processing, machine learning, and scientific …

Deep learning (DL) technology has made breakthroughs in a wide range of intelligent tasks
such as vision, language, recommendation systems, etc. Sparse matrix multiplication …

As the main workload of many scientific and machine learning applications, sparse matrix-
matrix multiplication (spGEMM) has become a hot research field. The current spG EMM …