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Least-Squares-Solver for Shallow Neural Network

  • Hantao Huang
  • Hao Yu
Chapter
Part of the Computer Architecture and Design Methodologies book series (CADM)

Abstract

This chapter presents a least-square based learning on the single hidden layer neural network. A square-root free Cholesky decomposition technique is applied to reduce the training complexity. Furthermore, the optimized learning algorithm is mapped on CMOS and RRAM based hardware. The two implementations on both RRAM and CMOS are presented. The detailed analysis of hardware implementation is discussed with significant speed-up and energy-efficiency improvement when compared with CPU and GPU based implementations (Figures and illustrations may be reproduced from [11, 12]).

Keywords

Machine learning Cholesky decomposition Neural network FPGA 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.School of Electrical and Electronic EngineeringNanyang Technological UniversitySingaporeSingapore
  2. 2.Department of Electrical and Electronic EngineeringSouthern University of Science and TechnologyShenzhenChina

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