Efficient and Robust High-Level Synthesis Design Space Exploration through offline Micro-kernels Pre-characterization

Abstract

This work proposes a method to accelerate the process of High-Level Synthesis (HLS) Design Space Exploration (DSE) by pre-characterizing micro-kernels offline and creating predictive models of these. HLS allows to generate different types of micro-architectures from the same untimed behavioral description. This is typically done by setting different combinations of synthesis options in the form or synthesis directives specified as pragmas in the code. This allows, e.g. to control how loops should be synthesized, arrays and functions. Unique combinations of these pragmas leads to micro-architectures with a unique area vs. performance/power trade-offs. The main problem is that the search space grows exponentially with the number of explorable operations. Thus, the main goal of efficient HLS DSE is to find the synthesis directives' combinations that lead to the Pareto-optimal designs quickly. Our proposed method is based on the pre-characterization of micro-kernels offline, creating predictive models for each of the kernels, and using the results to explore a new unseen behavioral description using compositional methods. In addition, we make use of perceptual hashing to match new unseen micro-kernels with the pre-characterized micro-kernels in order to further speed up the search process. Experimental results show that our proposed method is orders of magnitude faster than traditional methods.