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ApproxMA: Approximate Memory Access for Dynamic Precision Scaling

Published: 20 May 2015 Publication History

Abstract

Motivated by the inherent error-resilience of emerging recognition, mining, and synthesis (RMS) applications, approximate computing techniques such as precision scaling has been advocated for achieving energy-efficiency gains at the cost of small accuracy loss. Most existing solutions, however, focus on the approximation of on-chip computations without considering that of off-chip data accesses, whose energy consumption may contribute to a significant portion of the total energy. In this work, we propose a novel approximate memory access technique for dynamic precision scaling, namely ApproxMA. To be specific, by taking both runtime data precision constraints and error-resilient capabilities of the application into consideration, ApproxMA determines the precision of data accesses and loads scaled data from off-chip memory for computation. Experimental results with mixture model-based clustering algorithms demonstrate the efficacy of the proposed methodology.

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    cover image ACM Conferences
    GLSVLSI '15: Proceedings of the 25th edition on Great Lakes Symposium on VLSI
    May 2015
    418 pages
    ISBN:9781450334747
    DOI:10.1145/2742060
    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: 20 May 2015

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    Author Tags

    1. approximate computing
    2. memory access
    3. precision scaling

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    Cited By

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    • (2024)A Survey on Design Space Exploration Approaches for Approximate Computing SystemsElectronics10.3390/electronics1322444213:22(4442)Online publication date: 13-Nov-2024
    • (2024)Tunable Approximate Multiply Accumulate circuit2024 IEEE Region 10 Symposium (TENSYMP)10.1109/TENSYMP61132.2024.10752236(1-6)Online publication date: 27-Sep-2024
    • (2023)Approximate Computing: Hardware and Software Techniques, Tools and Their ApplicationsJournal of Circuits, Systems and Computers10.1142/S021812662430001033:04Online publication date: 20-Sep-2023
    • (2023)Approximate Softmax Functions for Energy-Efficient Deep Neural NetworksIEEE Transactions on Very Large Scale Integration (VLSI) Systems10.1109/TVLSI.2022.322401131:1(4-16)Online publication date: Jan-2023
    • (2023)A Survey of Approximate Computing: From Arithmetic Units Design to High-Level ApplicationsJournal of Computer Science and Technology10.1007/s11390-023-2537-y38:2(251-272)Online publication date: 30-Mar-2023
    • (2022)An Introduction to the Approximate Computing ParadigmApproximate Computing and its Impact on Accuracy, Reliability and Fault-Tolerance10.1007/978-3-031-15717-2_2(11-22)Online publication date: 17-Nov-2022
    • (2021)Energy Efficient Approximate MACs2021 IEEE 18th India Council International Conference (INDICON)10.1109/INDICON52576.2021.9691492(1-6)Online publication date: 19-Dec-2021
    • (2020)Survey on Approximate Computing and Its Intrinsic Fault ToleranceElectronics10.3390/electronics90405579:4(557)Online publication date: 26-Mar-2020
    • (2020)Multi-dimensional optimization for approximate near-threshold computingFrontiers of Information Technology & Electronic Engineering10.1631/FITEE.200008921:10(1426-1441)Online publication date: 25-Oct-2020
    • (2020)Exploiting Data Resilience in Wireless Network-on-chip ArchitecturesACM Journal on Emerging Technologies in Computing Systems10.1145/337944816:2(1-27)Online publication date: 4-Apr-2020
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