# Sparse Matrix to Matrix Multiplication: A Representation and Architecture for Acceleration (long version)

arXiv: https://arxiv.org/abs/1906.00327 · HTML: https://mlsanity.com/paper/1906.00327/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jun 2019.

## Built on by (most cited first, 2 of 2)

- [Two Sparsities Are Better Than One: Unlocking the Performance Benefits of Sparse-Sparse Networks](https://mlsanity.com/paper/2112.13896/) (2021)
- [Object-based Probabilistic Similarity Evidence of Sparse Latent Features from Fully Convolutional Networks](https://mlsanity.com/paper/2307.13606/) (2023)

## Abstract (arXiv)

Accelerators for sparse matrix multiplication are important components in emerging systems. In this paper, we study the main challenges of accelerating Sparse Matrix Multiplication (SpMM). For the situations that data is not stored in the desired order (row/column order), we propose a compact high performance sparse format, which allows for random access to a dataset with low memory access overhead. We show that using this format results in a 14-49 times speedup for SpMM. Next, we propose a high performance systolic architecture for SpMM, which uses a mesh of comparators to locate the useful (non-zero) computation. This design maximizes data reuse by sharing the input data among a row/column of the mesh. We also show that, with similar memory access assumptions, the proposed architecture results in a 9-30 times speedup in comparison with the state of the art.