# Matching Linear Algebra and Tensor Code to Specialized Hardware Accelerators

arXiv: https://arxiv.org/abs/2301.11659 · HTML: https://mlsanity.com/paper/2301.11659/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jan 2023.

## Built on by (most cited first, 3 of 3)

- [VEXIR2Vec: An Architecture-Neutral Embedding Framework for Binary Similarity](https://mlsanity.com/paper/2312.00507/) (2023)
- [Code Detection for Hardware Acceleration Using Large Language Models](https://mlsanity.com/paper/2307.10348/) (2023)
- [mlirSynth: Automatic, Retargetable Program Raising in Multi-Level IR using Program Synthesis](https://mlsanity.com/paper/2310.04196/) (2023)

## Abstract (arXiv)

Dedicated tensor accelerators demonstrate the importance of linear algebra in modern applications. Such accelerators have the potential for impressive performance gains, but require programmers to rewrite code using vendor APIs - a barrier to wider scale adoption. Recent work overcomes this by matching and replacing patterns within code, but such approaches are fragile and fail to cope with the diversity of real-world codes. We develop ATC, a compiler that uses program synthesis to map regions of code to specific APIs. The mapping space that ATC explores is combinatorially large, requiring the development of program classification, dynamic analysis, variable constraint generation and lexical distance matching techniques to make it tractable. We apply ATC to real-world tensor and linear algebra codes and evaluate them against four state-of-the-art approaches. We accelerate between 2.6x and 7x more programs, leading to over an order of magnitude performance improvement.