Combining module selection and replication for throughput-driven streaming programs

  • Authors:

  • Jason Cong;Muhuan Huang;Bin Liu;Peng Zhang;Yi Zou

  • Affiliations:

  • University of California, Los Angeles;University of California, Los Angeles;University of California, Los Angeles;University of California, Los Angeles;University of California, Los Angeles

  • Venue:
  • DATE '12 Proceedings of the Conference on Design, Automation and Test in Europe
  • Year:
  • 2012

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Abstract

Streaming processing is widely adopted in many data-intensive applications in various domains. FPGAs are commonly used to realize these applications since they can exploit inherent data parallelism and pipelining in the applications to achieve a better performance. In this paper we investigate the design space exploration problem (DSE) when mapping streaming applications onto FPGAs. Previous works narrowly focus on using techniques like replication or module selection to meet the throughput target. We propose to combine these two techniques together to guide the design space exploration. A formal formulation and solution to this combined problem is presented in this paper. Our objective is to optimize the total area cost subject to the throughput constraint. In particular, we are able to handle the feedback loops in the streaming programs, which, to the best of our knowledge, has never been discussed in previous work. Our methodology is evaluated with high-level synthesis tools, and we demonstrate our workflow on a set of benchmarks that vary from module kernel design such as FFT to large designs such as an MPEG-4 decoder.