@inproceedings{814fc6a0a6424e98acefe78a4b95fedc,
title = "Live demonstration: Face recognition on an ultra-low power event-driven convolutional neural network ASIC",
abstract = "We demonstrate an event-driven Deep Learning (DL) hardware software ecosystem. The user-friendly software tools port models from Keras (popular machine learning libraries), automaticaly convert DL models to Spiking equivalents, i.e. Spiking Convolutional Neural Networks (SCNNs) and run spiking simulations of the converted models on the hardware emulator for testing and prototyping. More importantly, the software ports the converted models onto a novel, ultra-low power, real-time, event-driven ASIC SCNN Chip: DynapCNN. An interactive demonstration of a real-time face recognition system built using the above pipeline is shown as an example.",
author = "Qian Liu and Ole Richter and Carsten Nielsen and Sadique Sheik and Giacomo Indiveri and Ning Qiao",
year = "2019",
month = jun,
doi = "10.1109/CVPRW.2019.00213",
language = "English",
series = "IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops",
publisher = "IEEE Computer Society",
pages = "1680--1681",
booktitle = "Proceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019",
note = "32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019 ; Conference date: 16-06-2019 Through 20-06-2019",
}