Clustering techniques for coarse-grained, antifuse FPGAs

  • Authors:
  • Chang Woo Kang;Massoud Pedram

  • Affiliations:
  • University of Southern California, Los Angeles, CA;University of Southern California, Los Angeles, CA

  • Venue:
  • Proceedings of the 2005 Asia and South Pacific Design Automation Conference
  • Year:
  • 2005

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Abstract

In this paper, we present area and performance-driven clustering techniques for coarse-grained, antifuse-based FPGAs. A macro logic cell in this class of FPGAs has multiple inputs and multiple outputs. Starting with this macro cell, a library of small logic cells can be generated and a target network was mapped with the library. For the minimum-area clustering, our algorithm minimizes the number of required macro logic cells to cover a network. Two linear equations were set up and we found the optimal mapping solution by using the equations. For the performance-driven clustering, the number of macro logic cells on the critical path is minimized by using the extension of Lawler's algorithm. The results show that the area-driven clustering algorithm reduced the number of macro logic cells by 12.29% and the performance-driven clustering reduced the maximum depth by 44.75% compared to a commercial tool.