Event-based PID controller fully realized in neuromorphic hardware: a one DoF study
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Date
2020
Publication Type
Conference Paper
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yes
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Abstract
Spiking Neuronal Networks (SNNs) realized in neuromorphic hardware lead to low-power and low-latency neuronal computing architectures. Neuromorphic computing systems are most efficient when all of perception, decision making, and motor control are seamlessly integrated into a single neuronal architecture that can be realized on the neuromorphic hardware. Many neuronal network architectures address the perception tasks, while work on neuronal motor controllers is scarce. Here, we present an improved implementation of a neuromorphic PID controller. The controller was realized on Intel's neuromorphic research chip Loihi and its performance tested on a drone, constrained to rotate on a single axis. The SNN controller is built using neuronal populations, in which a single spike carries information about sensed and control signals. Neuronal arrays perform computation on such sparse representations to calculate the proportional, derivative, and integral terms. The SNN PID controller is compared to a PID controller, implemented in software, and achieves a comparable performance, paving the way to a fully neuromorphic system in which perception, planning, and control are realized in an on-chip SNN.
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published
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Book title
2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Journal / series
Volume
Pages / Article No.
10939 - 10944
Publisher
IEEE
Event
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2020) (virtual)
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Methods
Software
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Date created
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Organisational unit
09699 - Indiveri, Giacomo / Indiveri, Giacomo
Notes
Due to the Coronavirus (COVID-19) the conference was conducted virtually.