Machine-learning based simulated annealer method for high level synthesis design space exploration

This paper presents a modified technique of simulated annealing, based on machine learning for effective multi-objective design space exploration in High Level Synthesis (HLS). In this work, we present a more efficient simulated annealing called Fast Simulated Annealer (FSA) which is based on a decision tree machine learning algorithm. Our proposed exploration method makes use of a standard simulated annealer to generate a training set, and uses this set to implement a decision tree. Based on the outcome of the decision tree, the algorithm fixes the synthesis directives (pragmas) which contribute to minimizing/maximizing one of the cost function objectives and continues the annealing procedure using the decision tree. Experimental results show that the average execution time of our proposed tree based simulated annealing algorithm is on average 36% faster than the standard annealer and can be up to 48% faster, while leading to similar results.