Translated Title

Low-power Face Detection Acceleration System Based on the Zynq Platform

Translated Abstract

To　address　the　issues　of　large　size　and　high　power　consumption　in　CPU-and　GPU-based　convolutional　neural　network　platforms,we　designed　and　implemented　a　convolutional　neural　network-assisted　face　detection　acceleration　system　based　on　the　Zynq　platform　in　this　study.We　adopted　the　YOLOv3-Tiny　algorithm　for　the　proposed　system　and　used　the　WIDER　FACE　dataset　for　training.To　improve　the　network　efficiency,we　utilized　a　layer-fusion　technique　for　reducing　the　network　depth　and　accelerating　detection.Moreover,we　employed　an　8-bit　integer　quantization　strategy　to　minimize　memory　access　and　resource　consumption.We　designed　a　reusable　multichannel　convolution　computation　module　by　leveraging　the　parallel　computing　capability　of　field-programmable　gate　arrays(FPGAs)on　the　ZynqXC7Z035　chip　to　reuse　the　digital　signal　processor(DSP).The　experimental　results　showed　that　our　designed　acceleration　system,which　could　achieve　a　real-time　inference　speed　of　9.5　FPS,was　7.9　times　faster　than　intel　i7-8700CPU　and　consumed　only　2.65　W　of　power,satisfying　the　performance　requirement　of　low　power　consumption.

Translated Keyword

convolutional neural network
FPGA
layer fusion
multichannel convolution
quantization

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Type

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Project No.

Access Number

WF:perioarticalbdtgd202403021

Language

Chinese

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