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

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019
PublisherIEEE Computer Society
Pages1680-1681
Number of pages2
ISBN (Electronic)9781728125060
DOIs
Publication statusPublished - Jun-2019
Event32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019 - Long Beach, United States
Duration: 16-Jun-201920-Jun-2019

Publication series

NameIEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Volume2019-June
ISSN (Print)2160-7508
ISSN (Electronic)2160-7516

Conference

Conference32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019
CountryUnited States
CityLong Beach
Period16/06/201920/06/2019

Cite this