Democratizing Domain-Specific Computing
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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.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 7 Sep 2022.
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Citing papers per year: 2023: 1; 2024: 1; 2025: 4.
6 papers.
- 2025HLS-Eval: A Benchmark and Framework for Evaluating LLMs on High-Level Synthesis Design Taskscited by 10
- 2025LIFT: LLM-Based Pragma Insertion for HLS via GNN Supervised Fine-Tuningcited by 8
- 2025iDSE: Navigating Design Space Exploration in High-Level Synthesis Using LLMscited by 7
- 2024Efficient Task Transfer for HLS DSEcited by 4
- 2023ProgSG: Cross-Modality Representation Learning for Programs in Electronic Design Automationcited by 0
- 2025DAPO: Design Structure-Aware Pass Ordering in High-Level Synthesis with Graph Contrastive and Reinforcement Learningcited by 0
- 2025DAPO: Design Structure-Aware Pass Ordering in High-Level Synthesis with Graph Contrastive and Reinforcement Learning12 Dec 2025
- 2025iDSE: Navigating Design Space Exploration in High-Level Synthesis Using LLMs28 May 2025
- 2025LIFT: LLM-Based Pragma Insertion for HLS via GNN Supervised Fine-Tuning29 Apr 2025
- 2025HLS-Eval: A Benchmark and Framework for Evaluating LLMs on High-Level Synthesis Design Tasks16 Apr 2025
- 2024Efficient Task Transfer for HLS DSE16 Aug 2024
- 2023ProgSG: Cross-Modality Representation Learning for Programs in Electronic Design Automation18 May 2023