Published September 20, 2018 | Version v1
Conference paper Open

Efficient Winograd-based Convolution Kernel Implementation on Edge Devices

  • 1. Intel Corporation, Ireland
  • 2. School of ECE, NTUA, Greece


The implementation of Convolutional Neural Networks on edge Internet of Things (IoT) devices is a significant programming challenge, due to the limited computational resources and the real-time requirements of modern applications. This work focuses on the efficient implementation of the Winograd convolution, based on a set of application-independent and Winograd-specific software techniques for improving the utilization of the edge devices computational resources. The proposed techniques were evaluated in Intel/Movidius Myriad2 platform, using 4 CNNs of various computational requirements. The results show significant performance improvements, up to 54%, over other convolution algorithms.



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