Live Demonstration: Face Recognition on an Ultra-Low Power Event-Driven Convolutional Neural Network ASIC

Qian Liu, Ole Richter, Carsten Nielsen, Sadique Sheik, Giacomo Indiveri, Ning Qiao; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2019, pp. 0-0

Abstract

The paper demonstrates an event-driven deep learning (DL) hardware software ecosystem. The user-friendly software tools port models from Keras (popular machine learning libraries), automaticly convert of DL models to Spiking equivalents, i.e. Spiking Neural Networks (SNNs) 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.

Related Material

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[bibtex]
@InProceedings{Liu_2019_CVPR_Workshops,
author = {Liu, Qian and Richter, Ole and Nielsen, Carsten and Sheik, Sadique and Indiveri, Giacomo and Qiao, Ning},
title = {Live Demonstration: Face Recognition on an Ultra-Low Power Event-Driven Convolutional Neural Network ASIC},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2019}
}