publication . Conference object . 2018

Efficient Winograd-based Convolution Kernel Implementation on Edge Devices

Athanasios Xygkis; Dimitrios Soudris; Lazaros Papadopoulos; Sofiane Yous; David Moloney;
Open Access
  • Published: 20 Sep 2018
  • Publisher: Zenodo
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.
free text keywords: Convolutional neural network, Computer engineering, Computer science, Kernel (linear algebra), Edge device, Convolution, Kernel (image processing), Software, business.industry, business
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Funded by
Software Development toolKit for Energy optimization and technical Debt elimination
  • Funder: European Commission (EC)
  • Project Code: 780572
  • Funding stream: H2020 | RIA
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Conference object . 2018
Providers: ZENODO
Conference object . 2018
Providers: Crossref
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