Approximate Computing: An Emerging Paradigm For Energy-Efficient Design

被引:0
|
作者
Han, Jie [1 ]
Orshansky, Michael [2 ]
机构
[1] Univ Alberta, Dept Elect & Comp Engn, Edmonton, AB, Canada
[2] Univ Texas Austin, Dept Elect & Comp Engn, Austin, TX USA
关键词
approximate computing; probabilistic computing; stochastic computation; adder; multiplier; low-energy design; COMPUTATION; ARCHITECTURE;
D O I
暂无
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Approximate computing has recently emerged as a promising approach to energy-efficient design of digital systems. Approximate computing relies on the ability of many systems and applications to tolerate some loss of quality or optimality in the computed result. By relaxing the need for fully precise or completely deterministic operations, approximate computing techniques allow substantially improved energy efficiency. This paper reviews recent progress in the area, including design of approximate arithmetic blocks, pertinent error and quality measures, and algorithm-level techniques for approximate computing.
引用
收藏
页数:6
相关论文
共 50 条
  • [1] Energy-efficient computing with approximate multipliers
    Pilipović, Ratko
    Bulić, Patricio
    Lotrič, Uroš
    [J]. Elektrotehniski Vestnik/Electrotechnical Review, 2022, 89 (03): : 117 - 123
  • [2] Energy-efficient computing with approximate multipliers
    Pilipovic, Ratko
    Bulic, Patricio
    Lotric, Uros
    [J]. ELEKTROTEHNISKI VESTNIK, 2022, 89 (03): : 117 - 123
  • [3] Energy-Efficient ConvNets Through Approximate Computing
    Moons, Bert
    De Brabandere, Bert
    Van Gool, Luc
    Verhelst, Marian
    [J]. 2016 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV 2016), 2016,
  • [4] Energy-Efficient Neural Computing with Approximate Multipliers
    Sarwar, Syed Shakib
    Venkataramani, Swagath
    Ankit, Aayush
    Raghunathan, Anand
    Roy, Kaushik
    [J]. ACM JOURNAL ON EMERGING TECHNOLOGIES IN COMPUTING SYSTEMS, 2018, 14 (02)
  • [5] Approximate High-Performance Computing: A Fast and Energy-Efficient Computing Paradigm in the Post-Moore Era
    Menon, Harshitha
    Diffenderfer, James
    Georgakoudis, Giorgis
    Laguna, Ignacio O.
    Lam, Michael
    Osei-Kuffuor, Daniel
    Parasyris, Konstantinos
    Vanover, Jackson
    [J]. IT PROFESSIONAL, 2023, 25 (02) : 7 - 15
  • [6] Approximate LSTM Computing for Energy-Efficient Speech Recognition
    Jo, Junseo
    Kung, Jaeha
    Lee, Youngjoo
    [J]. ELECTRONICS, 2020, 9 (12) : 1 - 13
  • [7] Approximate Computing: An Energy-Efficient Computing Technique for Error Resilient Applications
    Roy, Kaushik
    Raghunathan, Anand
    [J]. 2015 IEEE COMPUTER SOCIETY ANNUAL SYMPOSIUM ON VLSI, 2015, : 473 - 475
  • [8] Exploring Approximate Computing and Near-Threshold Operation to Design Energy-efficient Multipliers
    Zanandrea, Vinicius
    Borges, Douglas M.
    Rosa, Vagner S.
    Meinhardt, Cristina
    [J]. 34TH SBC/SBMICRO/IEEE/ACM SYMPOSIUM ON INTEGRATED CIRCUITS AND SYSTEMS DESIGN (SBCCI 2021), 2021,
  • [9] Cooptimization of Emerging Devices and Architectures for Energy-Efficient Computing
    Chen, An
    [J]. 2017 IEEE 12TH INTERNATIONAL CONFERENCE ON ASIC (ASICON), 2017, : 136 - 139
  • [10] VADF: Versatile Approximate Data Formats for Energy-Efficient Computing
    Mishra, Vishesh
    Mittal, Sparsh
    Hassan, Neelofar
    Singhal, Rekha
    Chatterjee, Urbi
    [J]. ACM TRANSACTIONS ON EMBEDDED COMPUTING SYSTEMS, 2023, 22 (05)