BDD-based logic synthesis for LUT-based FPGAs

  • Authors:
  • Navin Vemuri;Priyank Kalla;Russell Tessier

  • Affiliations:
  • Intel Corporation, Beaverton, OR;University of Utah, Salt Lake City, UT;University of Massachusetts, Amherst, MA

  • Venue:
  • ACM Transactions on Design Automation of Electronic Systems (TODAES)
  • Year:
  • 2002

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Abstract

Contemporary FPGA synthesis is a multiphase process that involves technology-independent logic optimization followed by FPGA-specific mapping to a target FPGA technology. Conventional technology-independent transformations target standard cells and are unable to optimize circuits with constraints and goals specific to FPGA architectures. This article describes an FPGA-specific logic synthesis approach, which unites multilevel logic transformation, decomposition, and optimization techniques into a single synthesis framework. This system performs network transformation, decomposition, and optimization at an early stage to generate a network that can be directly mapped onto FPGAs. Our techniques are built upon a BDD-based logic decomposition system. With this system, both AND-OR decompositions and AND-XOR decompositions can be identified, resulting in large area savings for synthesized XOR-intensive circuits. To induce good decompositions, a maximum fanout free cone (MFFC) -based partial clustering and collapsing technique is used. This step is followed by an area-minimizing variable partitioning heuristic that decomposes collapsed nodes into LUT-feasible subfunctions. As a postprocessing step, a performance-driven resynthesis phase is performed to alleviate increased delay caused by excessive logic sharing. We compare the quality of results obtained using our techniques with those of academic (BoolMap, SIS) and industry (Altera Quartus) FPGA synthesis tools. Experimental results indicate that the circuits generated by our techniques are not only smaller, but are also significantly faster than those synthesized by conventional FPGA synthesis tools. Furthermore, the computation times required by our techniques are significantly smaller than those of previous techniques.