Pattern-Mining for Behavioral Synthesis

Abstract

Pattern-based synthesis has drawn wide interest from researchers who tried to utilize the regularity in applications for design optimizations. In this letter, we present a general pattern-based behavior synthesis framework which can efficiently extract similar structures in programs. Our approach is very scalable in benefit of advanced pruning techniques. The similarity of structures is captured by a mismatch-tolerant metric: the graph edit distance. The graph edit distance can naturally capture different program variations such as bit-width, structure, and port variations. In addition, we further our approach to handle control-intensive applications, and this leads to more opportunities for optimization. Our algorithm uses a feature-based filtering approach for fast pruning, and a graph similarity metric called the generalized edit distance for measuring variations in control-data flow graphs. Furthermore, we apply our pattern-based synthesis system to the resource optimization problem in behavioral synthesis. Considering knowledge of discovered patterns, the resource binding step can intelligently generate the data-path to reduce interconnect costs. Experiments show that our approach can, on average, reduce the total area by about 20% with 7% latency overhead with our pattern techniques on the Xilinx Virtex-4 field-programmable gate arrays, compared to the traditional behavioral synthesis flow.