skip to main content
10.1145/3583781.3590265acmconferencesArticle/Chapter ViewAbstractPublication PagesglsvlsiConference Proceedingsconference-collections
research-article

IMAC:: A Pre-Multiplier And Integrated Reduction Based Multiply-And-Accumulate Unit

Published:05 June 2023Publication History

ABSTRACT

Multiply-and-accumulate (MAC) units are primarily utilized for convolution operations targeted towards signal and image processing workload. The compressors are applied at the partial product reduction stages to extract the multiplier output bits, which are later accumulated with an extra adder unit. The paper proposes an integrated approach where the other operand of the MAC unit is directly fed to the partial-product-matrix (PPM) before the product bits are evaluated. This integrated Multiplier-and-Accumulate (IMAC) approach saves an additional adder unit and instead extends the compressor, which is already used to reduce partial-product bits of the multiplier design. Compressors employed exact and approximate IMAC architectures were designed and evaluated through ASIC and FPGA flow. Five versions of inexact IMAC design were independently compared with traditional one-level approximation and two-level approximation in MAC designs. The proposed work is found to be hardware efficient when compared with state-of-art MAC units. The error metrics were either comparable or better for IMAC design when compared with separately designed approximate multipliers followed by exact or approximate adder units. The image blending application was considered to measure the quality metrics. The proposed IMAC design files are made freely available for further usage by the research and development community.

References

  1. Yashaswi Mannepalli, Viraj Bharadwaj Korede, and Madhav Rao. Novel approximate multiplier designs for edge detection application. In Proceedings of the 2021 on Great Lakes Symposium on VLSI, GLSVLSI '21, page 371--377, New York, NY, USA, 2021. Association for Computing Machinery.Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Swagath Venkataramani, Vivek J. Kozhikkottu, Amit Sabne, Kaushik Roy, and Anand Raghunathan. Logic synthesis of approximate circuits. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 39(10):2503--2515, 2020.Google ScholarGoogle ScholarCross RefCross Ref
  3. Shalini Singh, Pavan Kumar Pothula, and Madhav Rao. Design and evaluation of on-chip dct accelerators based on novel approximate reverse carry propagate adders. In 2022 IEEE Computer Society Annual Symposium on VLSI (ISVLSI), pages 8--13, 2022.Google ScholarGoogle ScholarCross RefCross Ref
  4. Vishesh Mishra, Divy Pandey, Saurabh Singh, Sagar Satapathy, Kaustav Goswami, Babita Jajodia, and Dip Sankar Banerjee. Art-mac: Approximate rounding and truncation based mac unit for fault-tolerant applications. In 2022 IEEE International Symposium on Circuits and Systems (ISCAS), pages 1640--1644, 2022.Google ScholarGoogle ScholarCross RefCross Ref
  5. Soujanya S R and Madhav Rao. Hardware characterization of integer-net based seizure detection models on fpga. In 2022 IEEE 15th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC), pages 224--231, 2022.Google ScholarGoogle Scholar
  6. Prashanth H C, Soujanya S R, Bindu G Gowda, and Madhav Rao. Design and evaluation of in-exact compressor based approximate multipliers. In Proceedings of the Great Lakes Symposium on VLSI 2022, GLSVLSI '22, page 431--436, New York, NY, USA, 2022. Association for Computing Machinery.Google ScholarGoogle Scholar
  7. Omkar G. Ratnaparkhi and Madhav Rao. Lead: Logarithmic exponent approximate divider for image quantization application. In Proceedings of the Great Lakes Symposium on VLSI 2022, GLSVLSI '22, page 437--442, New York, NY, USA, 2022. Association for Computing Machinery.Google ScholarGoogle Scholar
  8. Omkar G Ratnaparkhi and Madhav Rao. Esas: Exponent series based approximate square root design. In 2022 25th Euromicro Conference on Digital System Design (DSD), pages 39--45, 2022.Google ScholarGoogle ScholarCross RefCross Ref
  9. K J N S Bhargav, Sairam Palisetti, and Madhav Rao. A newton raphson method based approximate divider design for color quantization application. In 2021 18th International SoC Design Conference (ISOCC), pages 115--116, 2021.Google ScholarGoogle ScholarCross RefCross Ref
  10. Kunal Bharathi, Jiang Hu, and Sunil P. Khatri. Scaled population subtraction for approximate computing. In 2020 IEEE 38th International Conference on Computer Design (ICCD), pages 348--355, 2020.Google ScholarGoogle ScholarCross RefCross Ref
  11. H C Prashanth and Madhav Rao. Somalib: Library of exact and approximate activation functions for hardware-efficient neural network accelerators. In 2022 IEEE 40th International Conference on Computer Design (ICCD), pages 746--753, 2022.Google ScholarGoogle ScholarCross RefCross Ref
  12. Prashanth H. C.. and Madhav Rao. Improving digital circuit synthesis of complex functions using binary weighted fitness and variable mutation rate in cartesian genetic programming. In Proceedings of the 14th International Joint Conference on Computational Intelligence - ECTA,, pages 112--120. INSTICC, SciTePress, 2022.Google ScholarGoogle Scholar
  13. Nandagopal R, Rajashree V, and Madhav Rao. Accelerated piece-wise-linear implementation of floating-point power function. In 2022 29th IEEE International Conference on Electronics, Circuits and Systems (ICECS), pages 1--4, 2022.Google ScholarGoogle Scholar
  14. Alice Sokolova, Mohsen Imani, Andrew Huang, Ricardo Garcia, Justin Morris, Tajana Rosing, and Baris Aksanli. Maccelerator: Approximate arithmetic unit for computational acceleration. In 2021 22nd International Symposium on Quality Electronic Design (ISQED), pages 444--449, 2021.Google ScholarGoogle ScholarCross RefCross Ref
  15. Hang Xiao, Haobo Xu, Xiaoming Chen, Yujie Wang, and Yinhe Han. Fast and high-accuracy approximate mac unit design for cnn computing. IEEE Embedded Systems Letters, 14(3):155--158, 2022.Google ScholarGoogle ScholarCross RefCross Ref
  16. Gunho Park, Jaeha Kung, and Youngjoo Lee. Design and analysis of approximate compressors for balanced error accumulation in mac operator. IEEE Transactions on Circuits and Systems I: Regular Papers, 68(7):2950--2961, 2021.Google ScholarGoogle ScholarCross RefCross Ref
  17. Yicheng Lu, Weiwei Shan, and Jiaming Xu. A depthwise separable convolution neural network for small-footprint keyword spotting using approximate mac unit and streaming convolution reuse. In 2019 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS), pages 309--312, 2019.Google ScholarGoogle ScholarCross RefCross Ref
  18. Vojtech Mrazek, Zdenek Vasicek, Lukas Sekanina, Muhammad Abdullah Hanif, and Muhammad Shafique. Alwann: Automatic layer-wise approximation of deep neural network accelerators without retraining. In 2019 IEEE/ACM International Conference on Computer-Aided Design (ICCAD), pages 1--8, 2019.Google ScholarGoogle ScholarCross RefCross Ref
  19. Bahar Asgari, Ramyad Hadidi, and Hyesoon Kim. Meissa: Multiplying matrices efficiently in a scalable systolic architecture. In 2020 IEEE 38th International Conference on Computer Design (ICCD), pages 130--137, 2020.Google ScholarGoogle ScholarCross RefCross Ref
  20. Mingqiang Huang, Yucen Liu, Changhai Man, Kai Li, Quan Cheng, Wei Mao, and Hao Yu. A high performance multi-bit-width booth vector systolic accelerator for nas optimized deep learning neural networks. IEEE Transactions on Circuits and Systems I: Regular Papers, 69(9):3619--3631, 2022.Google ScholarGoogle ScholarCross RefCross Ref
  21. Wei Mao, Liuyao Dai, Kai Li, Quan Cheng, Yuhang Wang, Laimin Du, Shaobo Luo, Mingqiang Huang, and Hao Yu. An energy-efficient mixed-bitwidth systolic accelerator for nas-optimized deep neural networks. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 30(12):1878--1890, 2022.Google ScholarGoogle ScholarCross RefCross Ref
  22. G. A. Gillani, M. A. Hanif, B. Verstoep, S. H. Gerez, M. Shafique, and A. B. J. Kokkeler. Macish: Designing approximate mac accelerators with internal-self-healing. IEEE Access, 7:77142--77160, 2019.Google ScholarGoogle ScholarCross RefCross Ref
  23. Mahmoud Masadeh, Osman Hasan, and Sofiène Tahar. Input-conscious approximate multiply-accumulate (mac) unit for energy-efficiency. IEEE Access, 7:147129--147142, 2019.Google ScholarGoogle ScholarCross RefCross Ref
  24. Elizabeth Adams, Suganthi Venkatachalam, and Seok-Bum Ko. Energy-efficient approximate mac unit. In 2019 IEEE International Symposium on Circuits and Systems (ISCAS), pages 1--4, 2019.Google ScholarGoogle ScholarCross RefCross Ref
  25. https://sites.google.com/view/integratedmac/home.Google ScholarGoogle Scholar
  26. Yen-Jen Chang, Yu-Cheng Cheng, Shao-Chi Liao, and Chun-Huo Hsiao. A low power radix-4 booth multiplier with pre-encoded mechanism. IEEE Access, 8: 114842--114853, 2020.Google ScholarGoogle ScholarCross RefCross Ref
  27. Darjn Esposito, Antonio Giuseppe Maria Strollo, Ettore Napoli, Davide De Caro, and Nicola Petra. Approximate multipliers based on new approximate compressors. IEEE Transactions on Circuits and Systems I: Regular Papers, 65(12): 4169--4182, 2018.Google ScholarGoogle ScholarCross RefCross Ref

Index Terms

  1. IMAC:: A Pre-Multiplier And Integrated Reduction Based Multiply-And-Accumulate Unit

          Recommendations

          Comments

          Login options

          Check if you have access through your login credentials or your institution to get full access on this article.

          Sign in
          • Published in

            cover image ACM Conferences
            GLSVLSI '23: Proceedings of the Great Lakes Symposium on VLSI 2023
            June 2023
            731 pages
            ISBN:9798400701252
            DOI:10.1145/3583781

            Copyright © 2023 ACM

            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 the author(s) 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].

            Publisher

            Association for Computing Machinery

            New York, NY, United States

            Publication History

            • Published: 5 June 2023

            Permissions

            Request permissions about this article.

            Request Permissions

            Check for updates

            Qualifiers

            • research-article

            Acceptance Rates

            Overall Acceptance Rate312of1,156submissions,27%

            Upcoming Conference

            GLSVLSI '24
            Great Lakes Symposium on VLSI 2024
            June 12 - 14, 2024
            Clearwater , FL , USA
          • Article Metrics

            • Downloads (Last 12 months)93
            • Downloads (Last 6 weeks)2

            Other Metrics

          PDF Format

          View or Download as a PDF file.

          PDF

          eReader

          View online with eReader.

          eReader