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[2602.13261] A feedback control optimizer for online and hardware-aware training of Spiking Neural Networks
Summary
This article presents a novel feedback control optimizer for training Spiking Neural Networks (SNNs) on mixed-signal devices, addressing energy efficiency and scalability in neuromorphic computing.
Why It Matters
As the demand for energy-efficient computing grows, this research offers a significant advancement in optimizing Spiking Neural Networks for real-time applications. By integrating feedback control, the proposed method enhances the performance of SNNs, making them more viable for edge computing solutions.
Key Takeaways
- The proposed feedback control optimizer enables scalable on-chip learning for Spiking Neural Networks.
- Single-layer SNNs trained with this method achieve performance comparable to traditional artificial neural networks.
- The framework is designed for continuous online learning, enhancing resilience to hyperparameter mismatches.
Computer Science > Neural and Evolutionary Computing arXiv:2602.13261 (cs) [Submitted on 3 Feb 2026] Title:A feedback control optimizer for online and hardware-aware training of Spiking Neural Networks Authors:Matteo Saponati, Chiara De Luca, Giacomo Indiveri, Benjamin Grewe View a PDF of the paper titled A feedback control optimizer for online and hardware-aware training of Spiking Neural Networks, by Matteo Saponati and 3 other authors View PDF HTML (experimental) Abstract:Unlike traditional artificial neural networks (ANNs), biological neuronal networks solve complex cognitive tasks with sparse neuronal activity, recurrent connections, and local learning rules. These mechanisms serve as design principles in Neuromorphic computing, which addresses the critical challenge of energy consumption in modern computing. However, most mixed-signal neuromorphic devices rely on semi- or unsupervised learning rules, which are ineffective for optimizing hardware in supervised learning tasks. This lack of scalable solutions for on-chip learning restricts the potential of mixed-signal devices to enable sustainable, intelligent edge systems. To address these challenges, we present a novel learning algorithm for Spiking Neural Networks (SNNs) on mixed-signal devices that integrates spike-based weight updates with feedback control signals. In our framework, a spiking controller generates feedback signals to guide SNN activity and drive weight updates, enabling scalable and local on-chip l...
