Journal
IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS
Volume 26, Issue 8, Pages 1575-1579Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TVLSI.2018.2820016
Keywords
Custom computing; field-programmable gate array; neural network training; quasi-Newton (QN) method
Funding
- National Natural Science Foundation of China [61574099]
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In this brief, a customized and pipelined hardware implementation of the quasi-Newton (QN) method on field-programmable gate array (FPGA) is proposed for fast artificial neural networks onsite training, targeting at the embedded applications. The architecture is scalable to cope with different neural network sizes while it supports batch-mode training. Experimental results demonstrate the superior performance and power efficiency of the proposed implementation over CPU, graphics processing unit, and FPGA QN implementations.
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