ArticleProceedings of the National Academy of Sciences of the United States of America2022
Surrogate gradients for analog neuromorphic computing.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed.
- Dual-brain fusion: Bidirectional empowerment between human brain and brain-inspired systems.Fundamental research · 2026Review
- Neuromorphic Devices and Computing for Sensing, Memory, and Control.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Trainable neuromorphic spintronic hardware Via analog finite-difference gradient methods.Nature communications · 2026Article
- A highly energy-efficient multi-core neuromorphic architecture for training deep spiking neural networks.Nature communications · 2026Article
- Backpropagation through space, time and the brain.Nature communications · 2025Article
- Neural vs Neuromorphic Interfaces: Where Are We Standing?Chemical reviews · 2025Review
- Temporal single spike coding for effective transfer learning in spiking neural networks.Scientific reports · 2025Article
- Efficient and robust temporal processing with neural oscillations modulated spiking neural networks.Nature communications · 2025Article
- DelGrad: exact event-based gradients for training delays and weights on spiking neuromorphic hardware.Nature communications · 2025Article
- Training of physical neural networks.Nature · 2025Review
- Topology optimization of random memristors for input-aware dynamic SNN.Science advances · 2025Article
- The road to commercial success for neuromorphic technologies.Nature communications · 2025Review
- Quantum-limited stochastic optical neural networks operating at a few quanta per activation.Nature communications · 2025Article
- Article
- Application of Event Cameras and Neuromorphic Computing to VSLAM: A Survey.Biomimetics (Basel, Switzerland) · 2024Review
- Imitating and exploring the human brain's resting and task-performing states via brain computing: scaling and architecture.National science review · 2024Article
- Neuromorphic hardware for somatosensory neuroprostheses.Nature communications · 2024Review
- Temporal dendritic heterogeneity incorporated with spiking neural networks for learning multi-timescale dynamics.Nature communications · 2024Article
- The coming decade of digital brain research: A vision for neuroscience at the intersection of technology and computing.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Brain topology improved spiking neural network for efficient reinforcement learning of continuous control.Frontiers in neuroscience · 2024Article
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To rapidly process temporal information at a low metabolic cost, biological neurons integrate inputs as an analog sum, but communicate with spikes, binary events in time. Analog neuromorphic hardware uses the same principles to emulate spiking neural networks with exceptional energy efficiency. However, instantiating high-performing spiking networks on such hardware remains a significant challenge due to device mismatch and the lack of efficient training algorithms. Surrogate gradient learning has emerged as a promising training strategy for spiking networks, but its applicability for analog neuromorphic systems has not been demonstrated. Here, we demonstrate surrogate gradient learning on the BrainScaleS-2 analog neuromorphic system using an in-the-loop approach. We show that learning self-corrects for device mismatch, resulting in competitive spiking network performance on both vision and speech benchmarks. Our networks display sparse spiking activity with, on average, less than one spike per hidden neuron and input, perform inference at rates of up to 85,000 frames per second, and consume less than 200 mW. In summary, our work sets several benchmarks for low-energy spiking network processing on analog neuromorphic hardware and paves the way for future on-chip learning algorithms.
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