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Design and Analysis of Approximate Compressors for Balanced Error Accumulation in MAC Operator

Design and Analysis of Approximate Compressors for Balanced Error Accumulation in MAC Operator
Park, GunhoKung, JaehaLee, Youngjoo
DGIST Authors
Park, GunhoKung, JaehaLee, Youngjoo
Issued Date
Author Keywords
Approximate computingApproximate computingApproximation algorithmsCompressorsconvolutional neural networkEnergy consumptionHardwareimage processinglow-power circuit designMathematical modelmultiplier.Signal processing algorithms
Energy efficiencyEnergy utilizationComputational costsDesign and analysisEnergy efficientEnergy efficient computingError accumulationMultiply accumulateProbabilistic analysisRecognition accuracyErrors
In this paper, we present a novel approximate computing scheme suitable for realizing the energy-efficient multiply-accumulate (MAC) processing. In contrast to the prior works that suffer from the error accumulation limiting the approximate range, we utilize different approximate multipliers in an interleaved way to compensate errors in the opposite direction during accumulate operations. For the balanced error accumulation, we first design the approximate 4-2 compressors generating errors in the opposite direction while minimizing the computational costs. Based on the probabilistic analysis, positive and negative multipliers are then carefully developed to provide a similar error distance. Simulation results on various practical applications reveal that the proposed MAC processing offers the energy-efficient computing scenario by extending the range of approximate parts. Even compared to the state-of-the-art solutions, for example, the proposed interleaving scheme relaxes the core-level energy consumption of the recent CNN accelerator by more than 35% without degrading the recognition accuracy. IEEE
Institute of Electrical and Electronics Engineers
Related Researcher
  • 궁재하 Kung, Jaeha 전기전자컴퓨터공학과
  • Research Interests 딥러닝; 가속하드웨어; 저전력 하드웨어; 고성능 시스템
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Department of Electrical Engineering and Computer Science Intelligent Digital Systems Lab 1. Journal Articles


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