FPGA Implementations of Feed Forward Neural Network by using Floating Point Hardware Accelerators

doi:10.15598/aeee.v12i1.831

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Ho Chi Minh City University of Technology and Education, Vietnam

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DIMES Department of University of Calabria, Italy

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Ho Chi Minh City University of Transport, Vietnam

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Ho Chi Minh City University of Transport, Vietnam

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Ton Duc Thang University, Vietnam

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FPGA Implementations of Feed Forward Neural Network by using Floating Point Hardware Accelerators

Gabriele-Maria Lozito, Antonino Laudani, Francesco Riganti Fulginei, Alessandro Salvini

DOI: 10.15598/aeee.v12i1.831

Abstract

This paper documents the research towards the analysis of different solutions to implement a Neural Network architecture on a FPGA design by using floating point accelerators. In particular, two different implementations are investigated: a high level solution to create a neural network on a soft processor design, with different strategies for enhancing the performance of the process; a low level solution, achieved by a cascade of floating point arithmetic elements. Comparisons of the achieved performance in terms of both time consumptions and FPGA resources employed for the architectures are presented.