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Real-Time Meets Approximate Computing: An Elastic CNN Inference Accelerator with Adaptive Trade-off between QoS and QoR

Published: 18 June 2017 Publication History

Abstract

Due to the recent progress in deep learning and neural acceleration architectures, specialized deep neural network or convolutional neural network (CNNs) accelerators are expected to provide an energy-efficient solution for real-time vision/speech processing. recognition and a wide spectrum of approximate computing applications. In addition to their wide applicability scope, we also found that the fascinating feature of deterministic performance and high energy-efficiency, makes such deep learning (DL) accelerators ideal candidates as application-processor IPs in embedded SoCs concerned with real-time processing. However, unlike traditional accelerator designs, DL accelerators introduce a new aspect of design trade-off between real-time processing (QoS) and computation approximation (QoR) into embedded systems. This work proposes an elastic CNN acceleration architecture that automatically adapts to the hard QoS constraint by exploiting the error-resilience in typical approximate computing workloads For the first time, the proposed design, including network tuning-and-mapping software and reconfigurable accelerator hardware, aims to reconcile the design constraint of QoS and Quality of Result (QoR). which are respectively the key concerns in real-time and approximate computing. It is shown in experiments that the proposed architecture enables the embedded system to work flexibly in an expanded operating space, significantly enhances its real-time ability. and maximizes the energy-efficiency of system within the user-specified QoS-QoR constraint through self-reconfiguration.

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  • (2024)A Method for Swift Selection of Appropriate Approximate Multipliers for CNN Hardware Accelerators2024 IEEE International Symposium on Circuits and Systems (ISCAS)10.1109/ISCAS58744.2024.10558159(1-5)Online publication date: 19-May-2024
  • (2024)Approximate Full-Adders: A Comprehensive AnalysisIEEE Access10.1109/ACCESS.2024.346318212(136054-136072)Online publication date: 2024
  • (2023)Approximation Opportunities in Edge Computing Hardware: A Systematic Literature ReviewACM Computing Surveys10.1145/357277255:12(1-49)Online publication date: 3-Mar-2023
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cover image ACM Conferences
DAC '17: Proceedings of the 54th Annual Design Automation Conference 2017
June 2017
533 pages
ISBN:9781450349277
DOI:10.1145/3061639
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 18 June 2017

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Cited By

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  • (2024)A Method for Swift Selection of Appropriate Approximate Multipliers for CNN Hardware Accelerators2024 IEEE International Symposium on Circuits and Systems (ISCAS)10.1109/ISCAS58744.2024.10558159(1-5)Online publication date: 19-May-2024
  • (2024)Approximate Full-Adders: A Comprehensive AnalysisIEEE Access10.1109/ACCESS.2024.346318212(136054-136072)Online publication date: 2024
  • (2023)Approximation Opportunities in Edge Computing Hardware: A Systematic Literature ReviewACM Computing Surveys10.1145/357277255:12(1-49)Online publication date: 3-Mar-2023
  • (2023)Network Pruning for Bit-Serial AcceleratorsIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2022.320395542:5(1597-1609)Online publication date: May-2023
  • (2022)A Fast Precision Tuning Solution for Always-On DNN AcceleratorsIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2021.308966741:5(1236-1248)Online publication date: May-2022
  • (2022)Multi-Precision Deep Neural Network Acceleration on FPGAsProceedings of the 27th Asia and South Pacific Design Automation Conference10.1109/ASP-DAC52403.2022.9712485(454-459)Online publication date: 17-Jan-2022
  • (2022)Fx-Net and PureNetComputers in Biology and Medicine10.1016/j.compbiomed.2022.105913148:COnline publication date: 1-Sep-2022
  • (2021)An Energy-Efficient Inference Method in Convolutional Neural Networks Based on Dynamic Adjustment of the Pruning LevelACM Transactions on Design Automation of Electronic Systems10.1145/346097226:6(1-20)Online publication date: 1-Aug-2021
  • (2021)Reliability Evaluation and Analysis of FPGA-Based Neural Network Acceleration SystemIEEE Transactions on Very Large Scale Integration (VLSI) Systems10.1109/TVLSI.2020.304607529:3(472-484)Online publication date: Mar-2021
  • (2021)Nonconventional Computer Arithmetic Circuits, Systems and ApplicationsIEEE Circuits and Systems Magazine10.1109/MCAS.2020.302742521:1(6-40)Online publication date: Sep-2022
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