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Integrate-and-Fire Neurons for Low-Powered Pattern Recognition

Bacho, Florian, Chu, Dominique (2021) Integrate-and-Fire Neurons for Low-Powered Pattern Recognition. In: Lecture Notes in Computer Science. Artificial Intelligence and Soft Computing. 20th International Conference, ICAISC 2021, Virtual Event, June 21–23, 2021, Proceedings, Part I. . Springer, Cham, Switzerland ISBN 978-3-030-87985-3. (doi:10.1007/978-3-030-87986-0_3) (KAR id:89756)

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Embedded systems acquire information about the real world from sensors and process it to make decisions and/or for transmission. In some situations, the relationship between the data and the decision is complex and/or the amount of data to transmit is large (e.g. in biologgers). Artificial Neural Networks (ANNs) can efficiently detect patterns in the input data which makes them suitable for decision making or compression of information for data transmission. However, ANNs require a substantial amount of energy which reduces the lifetime of battery-powered devices. Therefore, the use of Spiking Neural Networks can improve such systems by providing a way to efficiently process sensory data without being too energy-consuming. In this work, we introduce a low-powered neuron model called Integrate-and-Fire which exploits the charge and discharge properties of the capacitor. Using parallel and series RC circuits, we developed a trainable neuron model that can be expressed in a recurrent form. Finally, we trained its simulation with an artificially generated dataset of dog postures and implemented it as hardware that showed promising energetic properties.

Item Type: Conference or workshop item (Proceeding)
DOI/Identification number: 10.1007/978-3-030-87986-0_3
Uncontrolled keywords: Remote System, Spiking Neural Networks, Integrate-And- Fire, Neuromorphic hardware
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Florian Bacho
Date Deposited: 13 Aug 2021 07:54 UTC
Last Modified: 04 Oct 2022 23:00 UTC
Resource URI: (The current URI for this page, for reference purposes)
Chu, Dominique:
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