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CoOAx: Correlation-aware Synthesis of FPGA-based Approximate Operators

Authors:

Siva Satyendra Sahoo,

Akash KumarAuthors Info & Claims

GLSVLSI '23: Proceedings of the Great Lakes Symposium on VLSI 2023

Pages 671 - 677

https://doi.org/10.1145/3583781.3590222

Published: 05 June 2023 Publication History

Abstract

The run-time reconfigurability and high parallelism offered by Field Programmable Gate Arrays (FPGAs) make them an attractive choice for implementing hardware accelerators for Machine Learning (ML) algorithms. In the quest for designing efficient FPGA-based hard-ware accelerators for ML algorithms, the inherent error-resilience of ML algorithms can be exploited to implement approximate hard-ware accelerators to trade the output accuracy with better over-all performance. As multiplication and addition are the two main arithmetic operations in ML algorithms, most state-of-the-art approximate accelerators have considered approximate architectures for these operations. However, these works have mainly considered the exploration and selection of approximate operators from an existing set of operators. To this end, we provide an efficient methodology for synthesizing and implementing novel approximate operators. Specifically, we propose a novel operator synthesis approach that supports multiple operator algorithms to provide new approximate multiplier and adder designs for AI inference applications. We report up to 27% and 25% lower power than state-of-the-art approximate designs, with equivalent error behavior, for 8-bit unsigned adders and 4-bit signed multipliers respectively. Further, we propose a correlation-aware Design Space Exploration (DSE) method that can improve the efficacy of randomized search algorithms in synthesizing novel approximate operators.

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Cited By

Sahoo SUllah SBhattacharjee SKumar A(2024) AxOCS : Scaling FPGA-Based Approximate Operators Using Configuration Supersampling IEEE Transactions on Circuits and Systems I: Regular Papers10.1109/TCSI.2024.338533371:6(2646-2659)Online publication date: Jun-2024
https://doi.org/10.1109/TCSI.2024.3385333
Ullah SSahoo SKumar A(2024) AxOSpike : Spiking Neural Networks-Driven Approximate Operator Design IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2024.344300043:11(3324-3335)Online publication date: Nov-2024
https://doi.org/10.1109/TCAD.2024.3443000
Ullah SSahoo SKumar A(2024)Enabling Energy-efficient AI Computing: Leveraging Application-specific Approximations : (Education Class)2024 International Conference on Compilers, Architecture, and Synthesis for Embedded Systems (CASES)10.1109/CASES60062.2024.00014(3-4)Online publication date: 29-Sep-2024
https://doi.org/10.1109/CASES60062.2024.00014
Show More Cited By

Index Terms

CoOAx: Correlation-aware Synthesis of FPGA-based Approximate Operators
1. Computer systems organization
  1. Embedded and cyber-physical systems
    1. Embedded systems
      1. Embedded hardware
2. Hardware
  1. Electronic design automation
    1. Logic synthesis
      1. Circuit optimization
  2. Integrated circuits
    1. Logic circuits
      1. Arithmetic and datapath circuits
    2. Reconfigurable logic and FPGAs
      1. Hardware accelerators

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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

General Chairs:
Himanshu Thapliyal
University of Tennessee, Knoxville, USA
,
Ronald DeMara
University of Central Florida, USA
,
Program Chairs:
Inna Partin-Vaisband
University of Illinois Chicago, USA
,
Srinivas Katkoori
University of South Florida, USA

Copyright © 2023 ACM.

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Publication History

Published: 05 June 2023

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Deutsche Forschungsgemeinschaft (DFG)

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GLSVLSI '23

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SIGDA

GLSVLSI '23: Great Lakes Symposium on VLSI 2023

June 5 - 7, 2023

TN, Knoxville, USA

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Cited By

Sahoo SUllah SBhattacharjee SKumar A(2024) AxOCS : Scaling FPGA-Based Approximate Operators Using Configuration Supersampling IEEE Transactions on Circuits and Systems I: Regular Papers10.1109/TCSI.2024.338533371:6(2646-2659)Online publication date: Jun-2024
https://doi.org/10.1109/TCSI.2024.3385333
Ullah SSahoo SKumar A(2024) AxOSpike : Spiking Neural Networks-Driven Approximate Operator Design IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2024.344300043:11(3324-3335)Online publication date: Nov-2024
https://doi.org/10.1109/TCAD.2024.3443000
Ullah SSahoo SKumar A(2024)Enabling Energy-efficient AI Computing: Leveraging Application-specific Approximations : (Education Class)2024 International Conference on Compilers, Architecture, and Synthesis for Embedded Systems (CASES)10.1109/CASES60062.2024.00014(3-4)Online publication date: 29-Sep-2024
https://doi.org/10.1109/CASES60062.2024.00014
Sahoo SUllah SKumar A(2023)AxOTreeS: A Tree Search Approach to Synthesizing FPGA-based Approximate OperatorsACM Transactions on Embedded Computing Systems10.1145/360909622:5s(1-26)Online publication date: 9-Sep-2023
https://dl.acm.org/doi/10.1145/3609096

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