Guac: Energy-Aware and SSA-Based Generation of Coarse-Grained Merged Accelerators from LLVM-IR
<|reference_start|>Guac: Energy-Aware and SSA-Based Generation of Coarse-Grained Merged Accelerators from LLVM-IR: Designing accelerators for resource- and power-constrained applications is a daunting task. High-level Synthesis (HLS) addresses these constraints through resource sharing, an optimization at the HLS binding stage that maps multiple operations to the same functional unit.   However, resource sharing is often limited to reusing instructions within a basic block. Instead of searching globally for the best control and dataflow graphs (CDFGs) to combine, it is constrained by existing instruction mappings and schedules.   Coarse-grained function merging (CGFM) at the intermediate representation (IR) level can reuse control and dataflow patterns without dealing with the post-scheduling complexity of mapping operations onto functional units, wires, and registers. The merged functions produced by CGFM can be translated to RTL by HLS, yielding Coarse Grained Merged Accelerators (CGMAs). CGMAs are especially profitable across applications with similar data- and control-flow patterns. Prior work has used CGFM to generate CGMAs without regard for which CGFM algorithms best optimize area, power, and energy costs.   We propose Guac, an energy-aware and SSA-based (static single assignment) CGMA generation methodology. Guac implements a novel ensemble of cost models for efficient CGMA generation. We also show that CGFM algorithms using SSA form to merge control- and dataflow graphs outperform prior non-SSA CGFM designs. We demonstrate significant area, power, and energy savings with respect to the state of the art. In particular, Guac more than doubles energy savings with respect to the closest related work while using a strong resource-sharing baseline.<|reference_end|>
@article{brumar2024guac:,
    title={Guac: Energy-Aware and SSA-Based Generation of Coarse-Grained Merged
  Accelerators from LLVM-IR},
    author={Iulian Brumar, Rodrigo Rocha, Alex Bernat, Devashree Tripathy, David
  Brooks, Gu-Yeon Wei},
    journal={arXiv preprint arXiv:2402.13513},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.13513},
    primaryClass={cs.AR}
}