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Exploring Data Size to Run Convolutional Neural Networks in Low Density FPGAs

  • Ana Gonçalves
  • Tiago Peres
  • Mário VéstiasEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11444)

Abstract

Convolutional Neural Networks (CNNs) obtain very good results in several computer vision applications at the cost of high computational and memory requirements. Therefore, CNN typically run on high performance platforms. However, CNNs can be very useful in embedded systems and its execution right next to the source of data has many advantages, like avoiding the need for data communication and real-time decisions turning these systems into smart sensors. In this paper, we explore data quantization for fast CNN inference in low density FPGAs. We redesign LiteCNN, an architecture for real-time inference of large CNN in low density FPGAs, to support hybrid quantization. We study the impact of quantization over the area, performance and accuracy of LiteCNN. LiteCNN with improved quantization of activations and weights improves the best state of the art results for CNN inference in low density FPGAs. With our proposal, it is possible to infer an image in AlexNet in 7.4 ms in a ZYNQ7020 and in 14.8 ms in a ZYNQ7010 with 3% accuracy degradation. Other delay versus accuracy ratios were identified permitting the designer to choose the most appropriate.

Keywords

Convolutional Neural Network FPGA Data quantization 

Notes

Acknowledgment

This work was supported by national funds through Fundação para a Ciência e a Tecnologia (FCT) with reference UID/CEC/50021/2019 and was also supported by project IPL/IDI&CA/2018/LiteCNN/ISEL through Instituto Politécnico de Lisboa.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.ISELInstituto Politécnico de LisboaLisbonPortugal
  2. 2.INESC-ID, ISELInstituto Politécnico de LisboaLisbonPortugal

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