Entropy-Driven Adaptive Filtering for High-Accuracy and Resource-Efficient FPGA-Based Neural Network Systems

Entropy-Driven Adaptive Filtering for High-Accuracy and Resource-Efficient FPGA-Based Neural Network Systems

Binarized neural networks are well suited for FPGA accelerators since their fine-grained architecture allows the creation of custom operators to support low-precision arithmetic operations, and the reduction in memory requirements means that all the network parameters can be stored in internal memor...

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Bibliographic Details
Main Authors:	Elim Yi Lam Kwan, Jose Nunez-Yanez
Format:	Article
Language:	English
Published:	MDPI AG 2020-10-01
Series:	Electronics
Subjects:	entropy adaptive filter high-accuracy FPGA resource-efficient FPGA real time object recognition software acceleration low precision network
Online Access:	https://www.mdpi.com/2079-9292/9/11/1765

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