ERI: Towards Efficient and Robust Federated Neuromorphic Learning in Wireless Edge Network — NSF Award to Kennesaw State Universit
Current distributed learning systems predominantly rely on artificial neural networks, which are generally energy-intensive. This issue is further exacerbated with the use of more advanced and larger models. In contrast, brain-inspired neuromorphic learning algorithms, such as spiking neural networks (SNNs), are renown
| Award title | ERI: Towards Efficient and Robust Federated Neuromorphic Learning in Wireless Edge Network |
|---|---|
| Award ID | 2501413 |
| Awardee | Kennesaw State University Research and Service Foundation |
| City | KENNESAW |
| State | GA |
| Amount obligated | $199,829 |
| Principal investigator | Liang Zhao |
| Program | ERI-Eng. Research Initiation |
| Start date | 10/01/2025 |
| Abstract | Current distributed learning systems predominantly rely on artificial neural networks, which are generally energy-intensive. This issue is further exacerbated with the use of more advanced and larger models. In contrast, brain-inspired neuromorphic learning algorithms, such as spiking neural networks (SNNs), are renowned for their energy efficiency, making them particularly promising for low-power edge applications. However, research on integrating SNNs with distributed learning remains scarce, |
| Source | NSF Awards |
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