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Spike-Based Plasticity Circuits for Always-on On-Line Learning in Neuromorphic Systems


Payvand, Melika; Indiveri, Giacomo (2019). Spike-Based Plasticity Circuits for Always-on On-Line Learning in Neuromorphic Systems. In: 2019 IEEE International Symposium on Circuits and Systems (ISCAS), Sapporo, Japan, 26 May 2019 - 29 May 2019.

Abstract

Event-driven neuromorphic hardware with on-line learning capabilities enables the low-power local processing of signals on the edge sensors. Implementing such hardware requires having an always-on online learning operation in order to continuously adapt to the changes in the environment. Therefore, as the data is continuously streaming, there cannot be a separation between the training and the testing phase. Such constraint thus asks for a continuous time learning strategy which includes a mechanism to stop changing the weights when the system has reached an optimal operating point, so that it does not over-fit the input data and it generalizes to unseen patterns of the learned class. In this paper we propose spike-based circuits based on a local gradient-descent based learning rule that comprise also this additional “stop-learning” feature and that have a wide range of configurability options over the learning parameters. We describe the circuit behavior and present simulation results for a standard CMOS 180 nm process, showing how the width of the stop-learning region can be controlled along with the learning rate of the system. Such system represents a hardware implementation of a feature which has shown to improves the stability of the learning process and the convergence properties of the network.

Abstract

Event-driven neuromorphic hardware with on-line learning capabilities enables the low-power local processing of signals on the edge sensors. Implementing such hardware requires having an always-on online learning operation in order to continuously adapt to the changes in the environment. Therefore, as the data is continuously streaming, there cannot be a separation between the training and the testing phase. Such constraint thus asks for a continuous time learning strategy which includes a mechanism to stop changing the weights when the system has reached an optimal operating point, so that it does not over-fit the input data and it generalizes to unseen patterns of the learned class. In this paper we propose spike-based circuits based on a local gradient-descent based learning rule that comprise also this additional “stop-learning” feature and that have a wide range of configurability options over the learning parameters. We describe the circuit behavior and present simulation results for a standard CMOS 180 nm process, showing how the width of the stop-learning region can be controlled along with the learning rate of the system. Such system represents a hardware implementation of a feature which has shown to improves the stability of the learning process and the convergence properties of the network.

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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
Scopus Subject Areas:Physical Sciences > Electrical and Electronic Engineering
Language:English
Event End Date:29 May 2019
Deposited On:11 Feb 2020 15:24
Last Modified:22 Apr 2020 23:01
Publisher:IEEE
ISBN:9781728103976
OA Status:Closed
Publisher DOI:https://doi.org/10.1109/iscas.2019.8702497

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