Predictable Accelerator Design with Time-Sensitive Affine Types
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Abstract (arXiv)
Field-programmable gate arrays (FPGAs) provide an opportunity to co-design applications with hardware accelerators, yet they remain difficult to program. High-level synthesis (HLS) tools promise to raise the level of abstraction by compiling C or C++ to accelerator designs. Repurposing legacy software languages, however, requires complex heuristics to map imperative code onto hardware structures. We find that the black-box heuristics in HLS can be unpredictable: changing parameters in the program that should improve performance can counterintuitively yield slower and larger designs. This paper proposes a type system that restricts HLS to programs that can predictably compile to hardware accelerators. The key idea is to model consumable hardware resources with a time-sensitive affine type system that prevents simultaneous uses of the same hardware structure. We implement the type system in Dahlia, a language that compiles to HLS C++, and show that it can reduce the size of HLS parameter spaces while accepting Pareto-optimal designs.
Key facts. Cited by 4 papers in the map (data through 13 Oct 2026). First posted on arXiv 9 Apr 2020.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 0; 2024: 1; 2025: 1; 2026: 1.
4 papers.
- 2024Allo: A Programming Model for Composable Accelerator Designcited by 16
- 2021Enabling Automated FPGA Accelerator Optimization Using Graph Neural Networkscited by 0
- 2025QiMeng: Fully Automated Hardware and Software Design for Processor Chipcited by 0
- 2026From Pragmas to Partners: A Symbiotic Evolution of Agentic High-Level Synthesiscited by 0
- 2026From Pragmas to Partners: A Symbiotic Evolution of Agentic High-Level Synthesis1 Feb 2026
- 2025QiMeng: Fully Automated Hardware and Software Design for Processor Chip5 Jun 2025
- 2024Allo: A Programming Model for Composable Accelerator Design7 Apr 2024
- 2021Enabling Automated FPGA Accelerator Optimization Using Graph Neural Networks17 Nov 2021