Abstract
In this paper, we focus on examining how scaling efficiency evolves in winner-take-all (WTA) network models on Intel Loihi neuromorphic processor, as network-related features such as network size, neuron type, and connectivity scheme change. By analyzing these relationships, our study aims to shed light on the intricate interplay between SNN features and the efficiency of neuromorphic systems as they scale up. The findings presented in this paper are expected to enhance the comprehension of scaling efficiency in neuromorphic hardware, providing valuable insights for researchers and developers in optimizing the performance of large-scale SNNs on neuromorphic architectures.
| Original language | English |
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| Title of host publication | ICECS 2023 - 2023 30th IEEE International Conference on Electronics, Circuits and Systems |
| Subtitle of host publication | Technosapiens for Saving Humanity |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 9798350326499 |
| DOIs | |
| Publication status | Published - 2023 |
| Event | 30th IEEE International Conference on Electronics, Circuits and Systems, ICECS 2023 - Istanbul, Turkey Duration: 4 Dec 2023 → 7 Dec 2023 |
Publication series
| Name | ICECS 2023 - 2023 30th IEEE International Conference on Electronics, Circuits and Systems: Technosapiens for Saving Humanity |
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Conference
| Conference | 30th IEEE International Conference on Electronics, Circuits and Systems, ICECS 2023 |
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| Country/Territory | Turkey |
| City | Istanbul |
| Period | 4/12/23 → 7/12/23 |
Bibliographical note
Publisher Copyright:© 2023 IEEE.
Funding
This work is supported by Intel’s Neuromorphic Research Community (INRC) Access Grant. We thank the INRC’s technical support team for helpful feedback on technical issues.
| Funders | Funder number |
|---|---|
| Intel’s Neuromorphic Research Community |
Keywords
- Intel Loihi
- Scaling Efficiency
- Spiking Neural Networks
- Winner-take-all