We propose ApproxHPVM, a compiler IR and system designed to enable accuracy-aware performance and energy tuning on heterogeneous systems with multiple compute units and approximation methods. ApproxHPVM automatically translates end-to-end application-level quality metrics into accuracy requirements for individual operations. ApproxHPVM uses a hardware-agnostic accuracy-tuning phase to do this translation that provides greater portability across heterogeneous hardware platforms and enables future capabilities like accuracy-aware dynamic scheduling and design space exploration.
ApproxHPVM incorporates three main components: (a) a compiler IR with hardware-agnostic approximation metrics, (b) a hardware-agnostic accuracy-tuning phase to identify error-tolerant computations, and (c) an accuracy-aware hardware scheduler that maps error-tolerant computations to approximate hardware components. As ApproxHPVM does not incorporate any hardware-specific knowledge as part of the IR, it can serve as a portable virtual ISA that can be shipped to all kinds of hardware platforms.
We evaluate our framework on nine benchmarks from the deep learning domain and five image processing benchmarks. Our results show that our framework can offload chunks of approximable computations to special-purpose accelerators that provide significant gains in performance and energy, while staying within user-specified application-level quality metrics with high probability. Across the 14 benchmarks, we observe from 1-9x performance speedups and 1.1-11.3x energy reduction for very small reductions in accuracy.
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Ulf AdamsGoogleLink to publication DOI
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|Optimization of Swift Protocols|
Raj BarikUber Technologies Inc., Manu SridharanUniversity of California Riverside, Murali Krishna RamanathanUber Technologies Inc., Milind ChabbiUber Technologies Inc.DOI
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|ApproxHPVM: A Portable Compiler IR for Accuracy-Aware Optimizations|
Hashim SharifUniversity of Illinois at Urbana-Champaign, Prakalp SrivastavaUniversity of Illinois at Urbana-Champaign, Muhammad HuzaifaUniversity of Illinois at Urbana-Champaign, Maria KotsifakouUniversity of Illinois at Urbana-Champaign, Keyur JoshiUniversity of Illinois at Urbana-Champaign, Yasmin SaritaCornell University, Nathan ZhaoUniversity of Illinois at Urbana-Champaign, Vikram S. AdveUniversity of Illinois at Urbana-Champaign, Sasa MisailovicUniversity of Illinois at Urbana-Champaign, Sarita AdveUniversity of Illinois at Urbana-ChampaignDOI
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|IVT: An Efficient Method for Sharing Subtype Polymorphic Objects|
Yu-Ping WangTsinghua University, China, Xu-Qiang HuTsinghua Univeraity, China, Zi-Xin ZouTsinghua Univeraity, China, Wende TanTsinghua University, China, Gang TanThe Pennsylvania State University, University Park, USADOI