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Robust Learning and Recognition of Visual Patterns in Neuromorphic Electronic Agents


Liang, Dongchen; Kreiser, Raphaela; Nielsen, Carsten; Qiao, Ning; Sandamirskaya, Yulia; Indiveri, Giacomo (2019). Robust Learning and Recognition of Visual Patterns in Neuromorphic Electronic Agents. In: 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Hsinchu, Taiwan, 18 March 2019 - 20 March 2019, IEEE.

Abstract

Mixed-signal analog/digital neuromorphic circuits are characterized by ultra-low power consumption, real-time processing abilities, and low-latency response times. These features make them promising for robotic applications that require fast and power-efficient computing. However, the unavoidable variance inherently existing in the analog circuits makes it challenging to develop neural processing architectures able to perform complex computations robustly. In this paper, we present a spiking neural network architecture with spike-based learning that enables robust learning and recognition of visual patterns in noisy silicon neural substrate and noisy environments. The architecture is used to perform pattern recognition and inference after a training phase with computers and neuromorphic hardware in the loop. We validate the proposed system in a closed-loop hardware setup composed of neuromorphic vision sensors and processors, and we present experimental results that quantify its real-time and robust perception and action behavior.

Abstract

Mixed-signal analog/digital neuromorphic circuits are characterized by ultra-low power consumption, real-time processing abilities, and low-latency response times. These features make them promising for robotic applications that require fast and power-efficient computing. However, the unavoidable variance inherently existing in the analog circuits makes it challenging to develop neural processing architectures able to perform complex computations robustly. In this paper, we present a spiking neural network architecture with spike-based learning that enables robust learning and recognition of visual patterns in noisy silicon neural substrate and noisy environments. The architecture is used to perform pattern recognition and inference after a training phase with computers and neuromorphic hardware in the loop. We validate the proposed system in a closed-loop hardware setup composed of neuromorphic vision sensors and processors, and we present experimental results that quantify its real-time and robust perception and action behavior.

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Additional indexing

Item Type:Conference or Workshop Item (Paper), not_refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Neuroinformatics
Dewey Decimal Classification:570 Life sciences; biology
Scopus Subject Areas:Physical Sciences > Artificial Intelligence
Physical Sciences > Hardware and Architecture
Physical Sciences > Electrical and Electronic Engineering
Language:English
Event End Date:20 March 2019
Deposited On:11 Feb 2020 15:00
Last Modified:27 Jan 2022 01:10
Publisher:IEEE
ISBN:9781538678848
OA Status:Closed
Publisher DOI:https://doi.org/10.1109/aicas.2019.8771580