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Open AccessFeature PaperArticle

Fast Convolutional Neural Networks in Low Density FPGAs Using Zero-Skipping and Weight Pruning

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INESC-ID, Instituto Superior de Engenharia de Lisboa, Instituto Politécnico de Lisboa, 1959-007 Lisbon, Portugal
2
INESC-ID, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisbon, Portugal
*
Author to whom correspondence should be addressed.
Electronics 2019, 8(11), 1321; https://doi.org/10.3390/electronics8111321
Received: 30 September 2019 / Revised: 5 November 2019 / Accepted: 6 November 2019 / Published: 9 November 2019
(This article belongs to the Section Computer Science & Engineering)
Edge devices are becoming smarter with the integration of machine learning methods, such as deep learning, and are therefore used in many application domains where decisions have to be made without human intervention. Deep learning and, in particular, convolutional neural networks (CNN) are more efficient than previous algorithms for several computer vision applications such as security and surveillance, where image and video analysis are required. This better efficiency comes with a cost of high computation and memory requirements. Hence, running CNNs in embedded computing devices is a challenge for both algorithm and hardware designers. New processing devices, dedicated system architectures and optimization of the networks have been researched to deal with these computation requirements. In this paper, we improve the inference execution times of CNNs in low density FPGAs (Field-Programmable Gate Arrays) using fixed-point arithmetic, zero-skipping and weight pruning. The developed architecture supports the execution of large CNNs in FPGA devices with reduced on-chip memory and computing resources. With the proposed architecture, it is possible to infer an image in AlexNet in 2.9 ms in a ZYNQ7020 and 1.0 ms in a ZYNQ7045 with less than 1% accuracy degradation. These results improve previous state-of-the-art architectures for CNN inference. View Full-Text
Keywords: deep learning; convolutional neural network; smart edge devices; zero-skipping; pruning; FPGA deep learning; convolutional neural network; smart edge devices; zero-skipping; pruning; FPGA
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Véstias, M.P.; Duarte, R.P.; de Sousa, J.T.; Neto, H.C. Fast Convolutional Neural Networks in Low Density FPGAs Using Zero-Skipping and Weight Pruning. Electronics 2019, 8, 1321.

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