A FPGA Friendly Approximate Computing Framework with Hybrid Neural Networks

Song, Haiyue; Song, Xiang; Li, Tianjian; Dong, Hao; Jing, Naifeng; Liang, Xiaoyao; Jiāng, Lì

doi:10.1145/3174243.3174965

Cited by 1 publication

(1 citation statement)

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“…Although this work focuses on CPUs, recent resiliency analyses on GPUs [55,62,83], for example, can potentially benefit from the concepts of Minotaur to improve runtime and/or accuracy. Approximate computing: Many techniques have been proposed that leverage approximate computing at the level of software [8,11,23,71,85,100,106,110,114,121,125,127], programming languages [15,20,74,86,87,101,102] and hardware [5,11,16,33,42,46,53,56,77,103,105,112,126,128,133] for improved performance, energy, or reliability. Criticality-testing [3,4,21,75,84,97,115] of approximate computations is important for many domains.…”

Section: Related Workmentioning

confidence: 99%

Minotaur

Mahmoud

Venkatagiri

Ahmed

et al. 2019

Proceedings of the Twenty-Fourth International Conference on Architectural Support for Programming Languages and Operating Syst

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With the end of conventional CMOS scaling, efficient resiliency solutions are needed to address the increased likelihood of hardware errors. Silent data corruptions (SDCs) are especially harmful because they can create unacceptable output without the user's knowledge. Several resiliency analysis techniques have been proposed to identify SDC-causing instructions, but they remain too slow for practical use and/or sacrifice accuracy to improve analysis speed. We develop Minotaur, a novel toolkit to improve the speed and accuracy of resiliency analysis. The key insight behind Minotaur is that modern resiliency analysis has many conceptual similarities to software testing; therefore, adapting techniques from the rich software testing literature can lead to principled and significant improvements in resiliency analysis. Minotaur identifies and adapts four concepts from software testing: 1) it introduces the concept of input quality criteria for resiliency analysis and identifies PC coverage as a simple but effective criterion; 2) it creates (fast) minimized inputs from (slow) standard benchmark inputs, using the input quality criteria to assess the goodness of the created input; 3) it adapts the concept of test case prioritization to prioritize error injections and invoke early termination for a given instruction to speed up error-injection campaigns; and 4) it further adapts test case or input prioritization to accelerate SDC discovery across multiple inputs. We evaluate Minotaur by applying it to Approxilyzer, a state-of-the-art resiliency analysis tool. Minotaur's first three techniques speed up Approxilyzer's resiliency analysis by

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