一句话：提出 Gamma，利用 Gustavson 算法设计稀疏矩阵乘法加速器，解决现有数据流复用差与访存不规则问题。
稀疏矩阵-稀疏矩阵乘法（spMspM）广泛用于科学与机器学习应用，但在通用架构上效率低，因而适合加速器。现有 spMspM 加速器多采用内积或外积数据流，存在输入或输出复用差的问题，导致高访存流量和低性能。本文提出 Gamma，将 Gustavson 算法引入 spMspM 加速器设计。Gustavson 算法可避免上述复用问题，但其不规则访存模式是此前加速器不支持的。摘要未给出具体实验评估与性能结果，核心贡献在于指出并利用这一替代数据流来设计支持不规则访问的加速器。
Sparse matrix-sparse matrix multiplication (spMspM) is at the heart of a wide range of scientific and machine learning applications. spMspM is inefficient on general-purpose architectures, making accelerators attractive. However, prior spMspM accelerators use inner- or outer-product dataflows that suffer poor input or output reuse, leading to high traffic and poor performance. These prior accelerators have not explored Gustavson's algorithm, an alternative spMspM dataflow that does not suffer from these problems but features irregular memory access patterns that prior accelerators do not support.
@inproceedings{paperbot6136,
title = {Gamma: leveraging Gustavson’s algorithm to accelerate sparse matrix multiplication},
author = {Guowei Zhang and Nithya Attaluri and Joel S. Emer and Daniel Sanchez},
booktitle = {ASPLOS},
year = {2021},
doi = {10.1145/3445814.3446702}
}