# Democratizing Domain-Specific Computing

arXiv: https://arxiv.org/abs/2209.02951 · HTML: https://mlsanity.com/paper/2209.02951/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Sep 2022.

## Built on by (most cited first, 6 of 6)

- [HLS-Eval: A Benchmark and Framework for Evaluating LLMs on High-Level Synthesis Design Tasks](https://mlsanity.com/paper/2504.12268/) (2025)
- [LIFT: LLM-Based Pragma Insertion for HLS via GNN Supervised Fine-Tuning](https://mlsanity.com/paper/2504.21187/) (2025)
- [iDSE: Navigating Design Space Exploration in High-Level Synthesis Using LLMs](https://mlsanity.com/paper/2505.22086/) (2025)
- [Efficient Task Transfer for HLS DSE](https://mlsanity.com/paper/2408.13270/) (2024)
- [ProgSG: Cross-Modality Representation Learning for Programs in Electronic Design Automation](https://mlsanity.com/paper/2305.10838/) (2023)
- [DAPO: Design Structure-Aware Pass Ordering in High-Level Synthesis with Graph Contrastive and Reinforcement Learning](https://mlsanity.com/paper/2512.11342/) (2025)

## Abstract (arXiv)

In the past few years, domain-specific accelerators (DSAs), such as Google's Tensor Processing Units, have shown to offer significant performance and energy efficiency over general-purpose CPUs. An important question is whether typical software developers can design and implement their own customized DSAs, with affordability and efficiency, to accelerate their applications. This article presents our answer to this question.