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High-level synthesis (HLS) has been widely adopted as it significantlyimproves the hardware design productivity and enables efficient design spaceexploration (DSE). Existing HLS tools are built using compiler infrastructureslargely based on a single-level abstraction, such as LLVM. However, as HLSdesigns typically come with intrinsic structural or functional hierarchies,different HLS optimization problems are often better solved with differentlevels of abstractions. This paper proposes ScaleHLS, a new scalable andcustomizable HLS framework, on top of a multi-level compiler infrastructurecalled MLIR. ScaleHLS represents HLS designs at multiple representation levelsand provides an HLS-dedicated analysis and transform library to solve theoptimization problems at the suitable levels. Using this library, we provide aDSE engine to generate optimized HLS designs automatically. In addition, wedevelop an HLS C front-end and a C/C++ emission back-end to translate HLSdesigns into/from MLIR for enabling an end-to-end compilation flow.Experimental results show that, comparing to the baseline designs withoutmanual directives insertion and code-rewriting, that are only optimized byXilinx Vivado HLS, ScaleHLS improves the performances with amazingquality-of-results -- up to 768.1x better on computation kernel level programsand up to 3825.0x better on neural network models.

ScaleHLS: A New Scalable High-Level Synthesis Framework on Multi-Level Intermediate Representation