Live Demonstration: Real-time Event-driven Object Recognition on SpiNNaker

被引：0

作者：

Orchard, Garrick ^{[1
]}

Lagorce, Xavier ^{[2
,3
,4
]}

Posch, Christoph ^{[2
,3
,4
]}

Furber, Steve ^{[5
]}

Benosman, Ryad ^{[2
,3
,4
]}

Galluppi, Francesco ^{[2
,3
,4
]}

机构：

[1] Natl Univ Singapore, Singapore Inst Neurotechnol SINAPSE, Singapore 117548, Singapore

[2] Univ Paris 06, Sorbonne Univ, UMR S 968, Inst Vis, Paris, France

[3] INSERM, U968, Paris, France

[4] CNRS, UMR 7210, Paris, France

[5] Univ Manchester, Adv Processor Technol Grp, Manchester M13 9PL, Lancs, England

来源：

2015 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS (ISCAS) | 2015年

基金：

英国工程与自然科学研究理事会;

关键词：

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This live demonstration shows real-time visual object recognition based on a spiking neural network adaptation of the HMAX model running on a purely event-based computational hardware platform. Visual input to the system is provided by an ATIS spiking silicon retina sensor. A SpiNNaker board processes the event-encoded visual information from the scene. Using a Leaky Integrate-and-Fire (LIF) neuron model implemented on SpiNNaker, an event-driven, multi-layer network is created that performs real-time orientation extraction and recombination. In this demonstration, the network will be tuned to recognize complex objects such as printed characters.

引用

页码：1903 / 1903

页数：1

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