Improving high level synthesis optimization opportunity through polyhedral transformations

  • Authors:
  • Wei Zuo;Yun Liang;Peng Li;Kyle Rupnow;Deming Chen;Jason Cong

  • Affiliations:
  • University of Illinois at Urbana-Champaign, Urbana, IL, USA;Peking University, Beijing, China;Peking University, Beijing, China;Advanced Digital Science Center, Singapore, Singapore;University of Illinois at Urbana-Champaign, Urbanan, IL, USA;University of California, Los Angeles, Los Angeles, CA, USA

  • Venue:
  • Proceedings of the ACM/SIGDA international symposium on Field programmable gate arrays
  • Year:
  • 2013

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Abstract

High level synthesis (HLS) is an important enabling technology for the adoption of hardware accelerator technologies. It promises the performance and energy efficiency of hardware designs with a lower barrier to entry in design expertise, and shorter design time. State-of-the-art high level synthesis now includes a wide variety of powerful optimizations that implement efficient hardware. These optimizations can implement some of the most important features generally performed in manual designs including parallel hardware units, pipelining of execution both within a hardware unit and between units, and fine-grained data communication. We may generally classify the optimizations as those that optimize hardware implementation within a code block (intra-block) and those that optimize communication and pipelining between code blocks (inter-block). However, both optimizations are in practice difficult to apply. Real-world applications contain data-dependent blocks of code and communicate through complex data access patterns. Existing high level synthesis tools cannot apply these powerful optimizations unless the code is inherently compatible, severely limiting the optimization opportunity. In this paper we present an integrated framework to model and enable both intra- and inter-block optimizations. This integrated technique substantially improves the opportunity to use the powerful HLS optimizations that implement parallelism, pipelining, and fine-grained communication. Our polyhedral model-based technique systematically defines a set of data access patterns, identifies effective data access patterns, and performs the loop transformations to enable the intra- and inter-block optimizations. Our framework automatically explores transformation options, performs code transformations, and inserts the appropriate HLS directives to implement the HLS optimizations. Furthermore, our framework can automatically generate the optimized communication blocks for fine-grained communication between hardware blocks. Experimental evaluation demonstrates that we can achieve an average of 6.04X speedup over the high level synthesis solution without our transformations to enable intra- and inter-block optimizations.