ASAP: Automatic Synthesis of Area-Efficient and Precision-Aware CGRAs

Tan, Cheng; Tambe, Thierry; Zhang, Jeff; Fang, Bo; Geng, Tong; Wei, Gu-Yeon; Brooks, David; Tumeo, Antonino; Gopalakrishnan, Ganesh; Li, Ang

doi:10.1145/3524059.3532359

ASAP: Automatic Synthesis of Area-Efficient and Precision-Aware CGRAs

Conference · 27 June 2022

DOI:https://doi.org/10.1145/3524059.3532359· OSTI ID:1886255

Tan, Cheng ^[1]; Tambe, Thierry ^[2]; Zhang, Jeff ^[2]; Fang, Bo ^[3]; Geng, Tong ^[3]; Wei, Gu-Yeon ^[2]; Brooks, David ^[2]; Tumeo, Antonino ^[3]; Gopalakrishnan, Ganesh ^[4]; Li, Ang ^[3]

Microsoft
Harvard University
BATTELLE (PACIFIC NW LAB)
University of Utah

Coarse-grained reconfigurable accelerators (CGRAs) are a promising accelerator design choice that strikes a balance between performance and adaptability to different computing patterns across various applications domains. Designing a CGRA for a specific application domain involves enormous software/hardware engineering effort. Recent research works explore loop transformations, functional unit types, network topology, and memory size to identify optimal CGRA designs given a set of kernels from a specific application do- main. Unfortunately, the impact of functional units with different precision support has rarely been investigated. To address this gap, we propose ASAP – a hardware/software co-design framework that automatically identifies and synthesizes optimal precision-aware CGRA for a set of applications of interest. Our evaluation shows that ASAP generates specialized designs 3.2×, 4.21×, and 5.8× more efficient (in terms of performance per unit of energy or area) than non-specialized homogeneous CGRAs, for the scientific computing, embedded, and edge machine learning domains, respectively, with limited accuracy loss. Moreover, ASAP provides more efficient designs than other state-of-the-art synthesis frameworks for specialized CGRAs.

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Research Organization:: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)

Sponsoring Organization:: USDOE

DOE Contract Number:: AC05-76RL01830

OSTI ID:: 1886255

Report Number(s):: PNNL-SA-172791

Resource Relation:: Conference: Proceedings of the 36th ACM International Conference on Supercomputing (ICS 2022), June 28-30, 2022, Virtual, Online

Country of Publication:: United States

Language:: English

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