Customizing Neural Networks for Efficient FPGA Implementation

TitleCustomizing Neural Networks for Efficient FPGA Implementation
Publication TypeConference Paper
Year of Publication2017
AuthorsSamragh, M., M. Ghasemzadeh, and F. Koushanfar
Conference NameIEEE International Symposium on Field-Programmable Custom Computing Machines (FCCM)
PublisherIEEE
KeywordsDeep neural networks, Domain-customized computing, Platform-aware customization, Reconfigurable computing
Abstract

Abstract—We propose a novel end-to-end framework to customize execution of deep neural networks on FPGA platforms. Our framework employs a reconfigurable clustering approach that encodes the parameters of deep neural networks in accordance with the application’s accuracy requirement and the underlying platform constraints. The throughput of FPGA-based realizations of neural networks is often bounded by the memory access bandwidth. The use of encoded parameters reduces both the required memory bandwidth and the computational complexity of neural networks, increasing the effective throughput. Our framework enables systematic customization of encoded deep neural networks for different FPGA platforms. Proof-of-concept evaluations on four different applications demonstrate up to 9-fold reduction in memory footprint and 15-fold improvement in the operational throughput while the drop in accuracy remains below 0.1%.

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SamraghCustomizing.pdf3.65 MB

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