Efficient Design Space Exploration of OpenCL Kernels for FPGA Targets Using Black Box Optimization

Abstract

Nowadays, many industries are in favor of using intelligent design space exploration as opposed to brute-force analysis. In many applications, the design space is defined by multiple variables and their interactions. Although brute-force analysis is very simple, it is rarely scalable when the number of variables in the system increases. With the rising complexity of hardware designs, more intelligent approaches are needed to explore the design options. This paper proposes using smart meta-heuristic search algorithms such as Grey Wolf Optimization (GWO) in conjunction with Bayesian Optimization (BO) to solve this problem. We show that we can further reduce the design effort using a surrogate model that is created based on a novel hybrid GWO-BO method. The surrogate model is a useful abstraction to detect functional and physical inter-dependencies in the system in order to accurately predict its performance (e.g. throughput or latency). We evaluate our methodology and show that it can produce competitive results in order to find the best design variables that maximize the performance of the system. Finally, we compare our results with previous statistical and heuristic methods proposed in the literature and find that the proposed GWO-BO method always performs better than the other considered methods.