Conference paper Open Access

Efficient Winograd-based Convolution Kernel Implementation on Edge Devices

Xygkis, Athanasios; Papadopoulos, Lazaros; Moloney, David; Soudris, Dimitrios; Yous, Sofiane


Citation Style Language JSON Export

{
  "DOI": "10.1145/3195970.3196041", 
  "author": [
    {
      "family": "Xygkis, Athanasios"
    }, 
    {
      "family": "Papadopoulos, Lazaros"
    }, 
    {
      "family": "Moloney, David"
    }, 
    {
      "family": "Soudris, Dimitrios"
    }, 
    {
      "family": "Yous, Sofiane"
    }
  ], 
  "issued": {
    "date-parts": [
      [
        2018, 
        9, 
        20
      ]
    ]
  }, 
  "abstract": "<p>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.</p>", 
  "title": "Efficient Winograd-based Convolution Kernel Implementation on Edge Devices", 
  "type": "paper-conference", 
  "id": "3347180"
}
35
1,043
views
downloads
Views 35
Downloads 1,043
Data volume 365.7 MB
Unique views 33
Unique downloads 1,012

Share

Cite as