# Capstan: A Vector RDA for Sparsity

arXiv: https://arxiv.org/abs/2104.12760 · HTML: https://mlsanity.com/paper/2104.12760/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Apr 2021.

## Built on by (most cited first, 1 of 1)

- [FuseFlow: A Fusion-Centric Compilation Framework for Sparse Deep Learning on Streaming Dataflow](https://mlsanity.com/paper/2511.04768/) (2025)

## Abstract (arXiv)

This paper proposes Capstan: a scalable, parallel-patterns-based, reconfigurable dataflow accelerator (RDA) for sparse and dense tensor applications. Instead of designing for one application, we start with common sparse data formats, each of which supports multiple applications. Using a declarative programming model, Capstan supports application-independent sparse iteration and memory primitives that can be mapped to vectorized, high-performance hardware. We optimize random-access sparse memories with configurable out-of-order execution to increase SRAM random-access throughput from 32% to 80%. For a variety of sparse applications, Capstan with DDR4 memory is 18x faster than a multi-core CPU baseline, while Capstan with HBM2 memory is 16x faster than an Nvidia V100 GPU. For sparse applications that can be mapped to Plasticine, a recent dense RDA, Capstan is 7.6x to 365x faster and only 16% larger.