4 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…
A Low-Power Sparse Convolution Accelerator with Idle-First-Task-Assignment for Edge Vision
Jingyue Zhuge, Johannes Partzsch, Christian Mayr
In recent years, edge-vision monitoring systems for applications such as smart animal husbandry have faced strict tripartite constraints: maintaining input resolution under extreme…
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…