publication . Conference object . 2018

Efficient Winograd-based Convolution Kernel Implementation on Edge Devices

Sofiane Yous; Dimitrios Soudris; Lazaros Papadopoulos; Athanasios Xygkis; David Moloney;
Open Access
  • Published: 20 Sep 2018
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 oth...
free text keywords: Convolutional neural network, Kernel (image processing), Computer engineering, Edge device, Memory management, Kernel (linear algebra), Software, business.industry, business, Process variation, Convolution, Computer science
Related Organizations
Funded by
Software Development toolKit for Energy optimization and technical Debt elimination
  • Funder: European Commission (EC)
  • Project Code: 780572
  • Funding stream: H2020 | RIA
Any information missing or wrong?Report an Issue