10.1145/3195970.3196041
https://zenodo.org/records/3347180
oai:zenodo.org:3347180
Xygkis, Athanasios
Athanasios
Xygkis
Intel Corporation, Ireland
Papadopoulos, Lazaros
Lazaros
Papadopoulos
School of ECE, NTUA, Greece
Moloney, David
David
Moloney
Intel Corporation, Ireland
Soudris, Dimitrios
Dimitrios
Soudris
School of ECE, NTUA, Greece
Yous, Sofiane
Sofiane
Yous
Intel Corporation, Ireland
Efficient Winograd-based Convolution Kernel Implementation on Edge Devices
Zenodo
2018
2018-09-20
https://zenodo.org/communities/eu
Creative Commons Attribution 4.0 International
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.
European Commission
10.13039/501100000780
780572
Software Development toolKit for Energy optimization and technical Debt elimination