Sparse-coefficient polynomial approximations for hardware implementations

被引:0
|
作者
Brisebarre, N [1 ]
Muller, JM [1 ]
Tisserand, A [1 ]
机构
[1] Univ Lyon 1, LIP, Arenaire Project,CNRS,ENS Lyon, INRIA,ENS Lyon, F-69364 Lyon, France
关键词
D O I
暂无
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
This paper presents a method for automatic generation of best polynomial approximations dedicated to hardware implementation. The generated polynomial approximations lead to high-speed and small hardware operators because of the use of sparse coefficients (i.e. we include fixed strings of zeros in the binary representation of the coefficients). Two different solutions have been investigated for the generation of the sparse-coefficient polynomial approximations. Our first results show up to 47% smaller coefficients compared to standard minimax approximations for comparable accuracy.
引用
收藏
页码:532 / 535
页数:4
相关论文
共 50 条
  • [1] Hardware operators for function evaluation using sparse-coefficient polynomials
    Brisebarre, N.
    Muller, J. -M.
    Tisserand, A.
    Torres, S.
    ELECTRONICS LETTERS, 2006, 42 (25) : 1441 - 1442
  • [2] Learning Optimal Feedback Operators and their Sparse Polynomial Approximations
    Kunisch, Karl
    Vasquez-Varas, Donato
    Walter, Daniel
    JOURNAL OF MACHINE LEARNING RESEARCH, 2023, 24
  • [3] Hardware implementation trade-offs of polynomial approximations and interpolations
    Lee, Dong-U
    Cheung, Ray C. C.
    Luk, Wayne
    Villasenor, John D.
    IEEE TRANSACTIONS ON COMPUTERS, 2008, 57 (05) : 686 - 701
  • [4] Sparse Polynomial Approximations for Affine Parametric Saddle Point Problems
    Chen, Peng
    Ghattas, Omar
    VIETNAM JOURNAL OF MATHEMATICS, 2023, 51 (01) : 151 - 175
  • [5] Sparse Polynomial Approximations for Affine Parametric Saddle Point Problems
    Peng Chen
    Omar Ghattas
    Vietnam Journal of Mathematics, 2023, 51 : 151 - 175
  • [6] Elementary Functions Hardware Implementation Using Constrained Piecewise-Polynomial Approximations
    Strollo, Antonio Giuseppe Maria
    De Caro, Davide
    Petra, Nicola
    IEEE TRANSACTIONS ON COMPUTERS, 2011, 60 (03) : 418 - 432
  • [7] Structured Sparse Ternary Weight Coding of Deep Neural Networks for Efficient Hardware Implementations
    Boo, Yoonho
    Sung, Wonyong
    2017 IEEE INTERNATIONAL WORKSHOP ON SIGNAL PROCESSING SYSTEMS (SIPS), 2017,
  • [8] SPARSE BAYESIAN POLYNOMIAL CHAOS APPROXIMATIONS OF ELASTO-PLASTIC MATERIAL MODELS
    Rosic, Bojana
    Matthies, Hermann G.
    COMPUTATIONAL PLASTICITY XIV: FUNDAMENTALS AND APPLICATIONS, 2017, : 256 - 267
  • [9] QPA: A Quantization-Aware Piecewise Polynomial Approximation Methodology for Hardware-Efficient Implementations
    Geng, Haoran
    Chen, Xiaoliang
    Zhao, Ning
    Du, Yuan
    Du, Li
    IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS, 2023, 31 (07) : 931 - 944
  • [10] Generating optimal CUDA sparse matrix-vector product implementations for evolving GPU hardware
    El Zein, Ahmed H.
    Rendell, Alistair P.
    CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE, 2012, 24 (01): : 3 - 13