Abstract

This paper presents a dedicated High-Level Synthesis (HLS) Design Space Explorer (DSE) for FPGAs. C-based VLSI design has the advantage over conventional RTL design that it allows the generation of micro-architectures with unique area vs. performance trade-offs without having to modify the original behavioral description (in this work area vs. latency). This is typically done by modifying the Functional Unit (FU) constraint file or setting different synthesis directives e.g. unroll loops or synthesize arrays as RAM or registers. The result of the design space exploration is a set of Pareto-optimal designs. In this work, we first investigate the quality of the exploration results when using the results reported after HLS (in particular the area) to guide the explorer in finding Pareto-optimal designs. We found that due to the nature of how HLS tools pre-characterize, the area and delay of basic logic primitives and the FPGAs internal structure the area results are not accurate and hence making it necessary to perform a logic synthesis after each newly generated design. This in turn leads to unacceptable long running time. This work therefore presents a dedicated DSE for FPGAs based on a pruning with adaptive windowing method to extract the design candidates to be further (logic) synthesized after HLS. The adaptive windowing is based on a learning method inspired from Rival Penalized Competitive Learning (RPCL) model in order to classify which designs need to be synthesized to find the true Pareto-optimal designs. Results show that our method leads to similar results compared to an explorer which performs a logic synthesis for each newly generated design, while being much faster.

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