Assisting High-Level Synthesis Improve SpMV Benchmark Through Dynamic Dependence Analysis

Rafael Garibotti, Brandon Reagen, Yakun Sophia Shao, Gu Yeon Wei, David Brooks

Research output: Contribution to journalArticlepeer-review


Recent advances in high-level synthesis (HLS) have enabled an automatic means of generating register-transfer level from high-level specifications without compromising performance. HLS provides substantial improvements to productivity and is a promising solution to designing future heterogeneous chips consisting of dozens of unique IP blocks (i.e., hardware accelerators). Despite their impressive capabilities, HLS tools today are commonly used to target a small subset of workloads, i.e., ones with inordinately regular control flow and memory access patterns. The challenges of achieving high-quality hardware for irregular workloads stems from HLS relying on static analysis. Static analysis is overly conservative when dealing with non-uniform memory access and imbalanced workloads, and identifying the most appropriate parallelizing strategy. In this brief, we propose the use of dynamic analysis to generate higher quality designs using commercial HLS tools. Our evaluations show that with dynamic dependence analysis, HLS designs achieve 3.3 × performance improvement for the sparse matrix-vector multiply benchmark.

Original languageEnglish (US)
Article number8421281
Pages (from-to)1440-1444
Number of pages5
JournalIEEE Transactions on Circuits and Systems II: Express Briefs
Issue number10
StatePublished - Oct 2018


  • Hardware accelerators
  • SpMV benchmark
  • dynamic dependence analysis
  • high-level synthesis

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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