Application of the Optimised Pulse Width Modulation (PWM) Based Encoding-Decoding Algorithm for Forecasting with Spiking Neural Networks (SNNs)
Spiking Neural Networks (SNNs) are recognised for processing spatiotemporal information with ultra-low power consumption. However, applying a non-efficient encoding-decoding algorithm can counter the efficiency advantages of the SNNs. In this sense, this paper presents one-step ahead forecasting cen...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-07-01
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| Series: | Engineering Proceedings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-4591/68/1/41 |
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| Summary: | Spiking Neural Networks (SNNs) are recognised for processing spatiotemporal information with ultra-low power consumption. However, applying a non-efficient encoding-decoding algorithm can counter the efficiency advantages of the SNNs. In this sense, this paper presents one-step ahead forecasting centered on the application of an optimised encoding-decoding algorithm based on Pulse Width Modulation (PWM) for SNNs. The validation is carried out with sine-wave, 3 UCI and 1 available real-world datasets. The results show the practical disappearance of the computational and energy costs associated with the encoding and decoding phases (less than 2% of the total costs) and very satisfactory forecasting results (MAE lower than 0.0357) for any dataset. |
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| ISSN: | 2673-4591 |