Publication: Neuromorphic analog circuits for robust on-chip always-on learning in spiking neural networks
Neuromorphic analog circuits for robust on-chip always-on learning in spiking neural networks
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Rubino, A., Cartiglia, M., Payvand, M., & Indiveri, G. (2023, June 11). Neuromorphic analog circuits for robust on-chip always-on learning in spiking neural networks. Proceedings of the IEEE International Conference on Artificial Intelligence Circuits and Systems. 2023 IEEE 5th International Conference on Artificial Intelligence Circuits and Systems (AICAS), Hangzhou. https://doi.org/10.1109/aicas57966.2023.10168620
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Mixed-signal neuromorphic systems represent a promising solution for solving extreme-edge computing tasks without relying on external computing resources. Their spiking neural network circuits are optimized for processing sensory data on-line in continuous-time. However, their low precision and high variability can severely limit their performance. To address this issue and improve their robustness to inhomogeneities and noise in both their internal state variables and external input signals, we designed on-chip learning circuits with
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- Rubino, Arianna
- Cartiglia, Matteo
- Payvand, Melika
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Rubino, A., Cartiglia, M., Payvand, M., & Indiveri, G. (2023, June 11). Neuromorphic analog circuits for robust on-chip always-on learning in spiking neural networks. Proceedings of the IEEE International Conference on Artificial Intelligence Circuits and Systems. 2023 IEEE 5th International Conference on Artificial Intelligence Circuits and Systems (AICAS), Hangzhou. https://doi.org/10.1109/aicas57966.2023.10168620
