Fast Neural Network Training on FPGA Using Quasi-Newton Optimization Method | IEEE Journals & Magazine | IEEE Xplore

Fast Neural Network Training on FPGA Using Quasi-Newton Optimization Method


Abstract:

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 ...Show More

Abstract:

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.
Page(s): 1575 - 1579
Date of Publication: 20 April 2018

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