18107

Face Recognition with Hybrid Efficient Convolution Algorithms on FPGAs

Chuanhao Zhuge, Xinheng Liu, Xiaofan Zhang, Sudeep Gummadi, Jinjun Xiong, Deming Chen
University of Illinois Urbana-Champaign
arXiv:1803.09004 [cs.CV], (23 Mar 2018)

@article{zhuge2018face,

   title={Face Recognition with Hybrid Efficient Convolution Algorithms on FPGAs},

   author={Zhuge, Chuanhao and Liu, Xinheng and Zhang, Xiaofan and Gummadi, Sudeep and Xiong, Jinjun and Chen, Deming},

   year={2018},

   month={mar},

   archivePrefix={"arXiv"},

   primaryClass={cs.CV}

}

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Deep Convolutional Neural Networks have become a Swiss knife in solving critical artificial intelligence tasks. However, deploying deep CNN models for latency-critical tasks remains to be challenging because of the complex nature of CNNs. Recently, FPGA has become a favorable device to accelerate deep CNNs thanks to its high parallel processing capability and energy efficiency. In this work, we explore different fast convolution algorithms including Winograd and Fast Fourier Transform (FFT), and find an optimal strategy to apply them together on different types of convolutions. We also propose an optimization scheme to exploit parallelism on novel CNN architectures such as Inception modules in GoogLeNet. We implement a configurable IP-based face recognition acceleration system based on FaceNet using High-Level Synthesis. Our implementation on a Xilinx Ultrascale device achieves 3.75x latency speedup compared to a high-end NVIDIA GPU and surpasses previous FPGA results significantly.
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