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ICSE 2020

HeteroRefactor: refactoring for heterogeneous computing with FPGA

Jason Lau, Aishwarya Sivaraman, Qian Zhang, Muhammad Ali Gulzar, Jason Cong, Miryung Kim

PDFDOI 10.1145/3377811.3380340dblp

Abstract

Heterogeneous computing with field-programmable gate-arrays (FPGAs) has demonstrated orders of magnitude improvement in computing efficiency for many applications. However, the use of such platforms so far is limited to a small subset of programmers with specialized hardware knowledge. High-level synthesis (HLS) tools made significant progress in raising the level of programming abstraction from hardware programming languages to C/C++, but they usually cannot compile and generate accelerators for kernel programs with pointers, memory management, and recursion, and require manual refactoring to make them HLS-compatible. Besides, experts also need to provide heavily handcrafted optimizations to improve resource efficiency, which affects the maximum operating frequency, parallelization, and power efficiency.

BibTeX
@inproceedings{Lau-al:ICSE20,
  author    = {Jason Lau and
               Aishwarya Sivaraman and
               Qian Zhang and
               Muhammad Ali Gulzar and
               Jason Cong and
               Miryung Kim},
  title     = {{HeteroRefactor:} refactoring for heterogeneous computing with {FPGA}},
  booktitle = {ICSE},
  pages     = {493--505},
  publisher = {{ACM}},
  year      = {2020},
}

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