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Open AccessArticle

Energy-Efficient Architecture for CNNs Inference on Heterogeneous FPGA

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Department of Informatics, Modeling, Electronics and System Engineering, University of Calabria, 87036 Rende, Italy
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Department of Mechanical, Energy and Management Engineering, University of Calabria, 87036 Rende, Italy
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Author to whom correspondence should be addressed.
J. Low Power Electron. Appl. 2020, 10(1), 1; https://doi.org/10.3390/jlpea10010001
Received: 6 December 2019 / Revised: 20 December 2019 / Accepted: 21 December 2019 / Published: 24 December 2019
Due to the huge requirements in terms of both computational and memory capabilities, implementing energy-efficient and high-performance Convolutional Neural Networks (CNNs) by exploiting embedded systems still represents a major challenge for hardware designers. This paper presents the complete design of a heterogeneous embedded system realized by using a Field-Programmable Gate Array Systems-on-Chip (SoC) and suitable to accelerate the inference of Convolutional Neural Networks in power-constrained environments, such as those related to IoT applications. The proposed architecture is validated through its exploitation in large-scale CNNs on low-cost devices. The prototype realized on a Zynq XC7Z045 device achieves a power efficiency up to 135 Gops/W. When the VGG-16 model is inferred, a frame rate up to 11.8 fps is reached. View Full-Text
Keywords: convolutional neural networks; heterogeneous FPGAs; embedded systems convolutional neural networks; heterogeneous FPGAs; embedded systems
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Spagnolo, F.; Perri, S.; Frustaci, F.; Corsonello, P. Energy-Efficient Architecture for CNNs Inference on Heterogeneous FPGA. J. Low Power Electron. Appl. 2020, 10, 1.

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