High-level Synthesis for Domain Specific Computing

Hanchen Ye, Hyegang Jun, Jin Yang, Deming Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution


This paper proposes a High-Level Synthesis (HLS) framework for domain-specific computing. The framework contains three key components: 1) ScaleHLS, a multi-level HLS compilation flow. Aimed to address the lack of expressiveness and hardware-dedicated representation of traditional software-oriented compilers. ScaleHLS introduces a hierarchical intermediate representation (IR) for the progressive optimization of HLS designs defined in various high-level languages. ScaleHLS consists of three levels of optimizations, including graph, loop, and directive levels, to realize an efficient compilation pipeline and generate highly-optimized domain-specific accelerators. 2) AutoScaleDSE is an automated design space exploration (DSE) engine. Real-world HLS designs often come with large design spaces that are difficult for designers to explore. Meanwhile, the connections between different components of an HLS design further complicate the design spaces. In order to address the DSE problem, AutoScaleDSE proposes a random forest classifier and a graph-driven approach to improve the accuracy of estimating the intermediate DSE results while reducing the time and computational cost. With this new approach, AutoScaleDSE can evaluate thousands of HLS design points and find the Pareto-dominating design points within a couple of hours. 3) PyTransform is a flexible pattern-driven design customization flow. Existing HLS flows demand manual code rewriting or intrusive compiler customization to conduct domain-specific optimizations, leading to unscalable or inflexible compiler solutions. PyTransform proposes a Python-based flow that enables users to define custom matching and rewriting patterns at a high level of abstraction, being able to be incorporated into the DSL compilation flow in an automatic and scalable manner. In summary, ScaleHLS, AutoScaleDSE, and PyTransform aim to address the challenges present in the compilation, DSE, and customization of existing HLS flows, respectively. With the three key components, our newly proposed HLS framework can deliver a scalable and extensible solution for designing domain-specific languages to automate and speed up the process of designing domain-specific accelerators.

Original languageEnglish (US)
Title of host publicationISPD 2023 - Proceedings of the 2023 International Symposium on Physical Design
PublisherAssociation for Computing Machinery
Number of pages9
ISBN (Electronic)9781450399784
StatePublished - Mar 26 2023
Event32nd ACM International Symposium on Physical Design, ISPD 2023 - Virtual, Online, United States
Duration: Mar 26 2023Mar 29 2023

Publication series

NameProceedings of the International Symposium on Physical Design


Conference32nd ACM International Symposium on Physical Design, ISPD 2023
Country/TerritoryUnited States
CityVirtual, Online


  • Design space exploration
  • Domain-specific computing
  • Domain-specific language
  • HLS
  • MLIR

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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