| DOI | Resolve DOI: https://doi.org/10.1109/SEC54971.2022.00081 |
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| Author | Search for: Gaurav, Ramashish; Search for: Stewart, Terrence C.1; Search for: Yi, Yang Cindy |
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| Affiliation | - National Research Council of Canada. Digital Technologies
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| Format | Text, Article |
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| Conference | 2022 IEEE/ACM 7th Symposium on Edge Computing (SEC), December 5-8, 2022, Seattle, WA, USA |
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| Subject | spiking; reservoir; edge computing; loihi; energy consumption; neuromorphics; computational modeling; neural networks; hardware; energy efficiency |
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| Abstract | Low latency and low energy consumption are the indispensable characteristics of Edge Computing applications. With the fusion of Edge Computing and Artificial Intelligence (AI) into Edge Intelligence, this need is more than ever. Of late, Spiking Neural Networks have shown a promise for low latency and low power AI when deployed on a neuromorphic hardware e.g., Intel's Loihi. In this paper, we present a Spiking Reservoir Computing model, based on the Legendre Memory Units which processes temporal data on Loihi hardware. Such a model is greatly suitable for the battery-powered AI enabled edge devices which call for a prompt processing of the temporal sensor-signals with high energy efficiency. We experiment our model with the ECG5000 dataset on the Loihi boards to show its efficacy. |
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| Publication date | 2023-01-02 |
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| Publisher | IEEE |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | 59a26134-5444-41be-abc7-d4692677c909 |
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| Record created | 2023-05-15 |
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| Record modified | 2023-05-16 |
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