126 citations · 535 across the 19 of their papers we have counts for
20 papers
Synapse Loss Estimation for the BrainScaleS Wafer-scale Neuromorphic System
Bernhard Vogginger, Christian Mayr
Neuromorphic hardware combines memory (synapses) and computation (neurons) into the same silicon substrate to avoid the von-Neumann bottleneck. This way, neuromorphic computing aim…
The SpiNNaker2 chip: a many-core platform for flexible and scalable brain-inspired computing
Stefan Scholze, Johannes Partzsch, Sebastian Höppner +27
In deep learning, efficiency gets more and more important to compensate for the ongoing growth in model sizes and applications. Neuromorphic hardware has long been advocated as an…
Characterization of Off-wafer Pulse Communication in BrainScaleS Neuromorphic System
Bernhard Vogginger, Vasilis Thanasoulis, Johannes Partzsch +1
Neuromorphic VLSI systems take inspiration from biology to enable efficient emulation of large-scale spiking neural networks and to explore new computational paradigms. To establis…
Hardware-Aware Fine-Tuning of Spiking Q-Networks on the SpiNNaker2 Neuromorphic Platform
Sirine Arfa, Bernhard Vogginger, Christian Mayr
Spiking Neural Networks (SNNs) promise orders-of-magnitude lower power consumption and low-latency inference on neuromorphic hardware for a wide range of robotic tasks. In this wor…
Efficient Deployment of Spiking Neural Networks on SpiNNaker2 for DVS Gesture Recognition Using Neuromorphic Intermediate Representation
Sirine Arfa, Bernhard Vogginger, Chen Liu +3
Spiking Neural Networks (SNNs) are highly energy-efficient during inference, making them particularly suitable for deployment on neuromorphic hardware. Their ability to process eve…
Event-based backpropagation on the neuromorphic platform SpiNNaker2
Gabriel Béna, Timo Wunderlich, Mahmoud Akl +3
Neuromorphic computing aims to replicate the brain's capabilities for energy efficient and parallel information processing, promising a solution to the increasing demand for faster…