Compiler-Assisted Selection of Hardware Acceleration Candidates from Application Source Code

Abstract

Hardware design is a difficult task. Beside ensuring functional correctness of an implementation, hardware developers are confronted with multiple and often conflicting constraints, such as performance and area cost targets, that require lengthy explorations. This issue is compounded when considering the acceleration of complex applications, of which some parts are implemented in software, and others are accelerated in hardware. Hardware/Software partitioning must be settled early in the development cycle, and is far from trivial, since at this stage detailed performance measurements are not available, while wrong choices can lead to vastly sub-optimal solutions or to wasted implementation efforts. To address this challenge, we present a framework to automatically identify, from un-modified software code, software segments that are promising candidates for hardware acceleration, to evaluate their potential speedup and resource requirements, and to select a subset of them under resource constraint. Our strategy is based on Intermediate Representation (IR) analysis passes, which we embed in the LLVM compiler toolchain, and does not require any time-consuming synthesis. We explore its effectiveness on the reference software implementation of a complex application, the H.264 Decoder from University of Illinois, and demonstrate that our methodology selects higher-performance sets of accelerators, when compared to strategies only based on profiling information.