Machine Leaming to Set Meta-Heuristic Specific Parameters for High-Level Synthesis Design Space Exploration
Zi Wang, Benjamin Carrión Schäfer
Abstract
Raising the level of VLSI design abstraction to C leads to many advantages compared to the use of low-level Hardware Description Languages (HDLs). One key advantage is that it allows the generation of micro-architectures with different trade-offs by simply setting unique combinations of synthesis options. Because the number of these synthesis options is typically very large, exhaustive enumerations are not possible. Hence, heuristics are required. Meta-heuristics like Simulated Annealing (SA), Genetic Algorithm (GA) and Ant Colony Optimizations (ACO) have shown to lead to good results for these types of multi-objective optimization problems. The main problem with these meta-heuristics is that they are very sensitive to their hyper-parameter settings, e.g. in the GA case, the mutation and crossover rate and the number of parents pairs. To address this, in this work we present a machine learning based approach to automatically set the search parameters for these three meta-heuristics such that a new unseen behavioral description given in C can be effectively explored. Moreover, we present an exploration technique that combines the SA, GA and ACO together and show that our proposed exploration method outperforms a single meta-heuristic.