Design-Space Exploration for High-Level Synthesis

The design methodology presented in this paper simultaneously performs scheduling, allocation and module selection using problem-space genetic algorithm (PSGA) to optimize the three items: (1) the hardware resources (i.e., functional units, registers, and interconnection cost), (2) the number of control steps and (3) the length of the clock period. The proposed PSGA based approach uses the inherent parallelism provided by genetic algorithms and exploits the problem-specific knowledge by using a simple and fast heuristic to search a large design space effectively and efficiently. The proposed PSGA method offers several advantages such as the versatility, simplicity, objective independence and the computational advantages for problems of large size over other existing techniques. Experiments on benchmarks show very promising results.