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Event-based PID controller fully realized in neuromorphic hardware: a one DoF study


Stagsted, R K; Vitale, A; Renner, A; Larsen, L B; Christensen, A L; Sandamirskaya, Yulia (2020). Event-based PID controller fully realized in neuromorphic hardware: a one DoF study. In: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, USA, 25 October 2020 - 29 October 2020.

Abstract

Spiking Neuronal Networks (SNNs) realized inneuromorphic hardware lead to low-power and low-latencyneuronal computing architectures. Neuromorphic computingsystems are most efficient when all of perception, decision mak-ing, and motor control are seamlessly integrated into a singleneuronal architecture that can be realized on the neuromorphichardware. Many neuronal network architectures address theperception tasks, while work on neuronal motor controllersis scarce. Here, we present an improved implementation of aneuromorphic PID controller. The controller was realized onIntel’s neuromorphic research chip Loihi and its performancetested on a drone, constrained to rotate on a single axis. TheSNN controller is built using neuronal populations, in whicha single spike carries information about sensed and controlsignals. Neuronal arrays perform computation on such sparserepresentations to calculate the proportional, derivative, andintegral terms. The SNN PID controller is compared to a PIDcontroller, implemented in software, and achieves a comparableperformance, paving the way to a fully neuromorphic systemin which perception, planning, and control are realized in anon-chip SNN.

Abstract

Spiking Neuronal Networks (SNNs) realized inneuromorphic hardware lead to low-power and low-latencyneuronal computing architectures. Neuromorphic computingsystems are most efficient when all of perception, decision mak-ing, and motor control are seamlessly integrated into a singleneuronal architecture that can be realized on the neuromorphichardware. Many neuronal network architectures address theperception tasks, while work on neuronal motor controllersis scarce. Here, we present an improved implementation of aneuromorphic PID controller. The controller was realized onIntel’s neuromorphic research chip Loihi and its performancetested on a drone, constrained to rotate on a single axis. TheSNN controller is built using neuronal populations, in whicha single spike carries information about sensed and controlsignals. Neuronal arrays perform computation on such sparserepresentations to calculate the proportional, derivative, andintegral terms. The SNN PID controller is compared to a PIDcontroller, implemented in software, and achieves a comparableperformance, paving the way to a fully neuromorphic systemin which perception, planning, and control are realized in anon-chip SNN.

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Additional indexing

Item Type:Conference or Workshop Item (Paper), refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Neuroinformatics
Dewey Decimal Classification:570 Life sciences; biology
Language:English
Event End Date:29 October 2020
Deposited On:16 Feb 2021 08:32
Last Modified:16 Feb 2021 20:30
Publisher:IEEE
OA Status:Green

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