| year | 2022 |
| author(s) | Pehle C, Billaudelle S, Cramer B, Kaiser J, Schreiber K, Stradmann Y, Weis J, Leibfried A, Müller E and Schemmel J |
| title | The BrainScaleS-2 Accelerated Neuromorphic System With Hybrid Plasticity |
| KIP-Nummer | HD-KIP 22-31 |
| KIP-Gruppe(n) | F9 |
| document type | Paper |
| Keywords (shown) | neuromorphic computing, physical modeling, neuroscientific modeling, spiking neural network accelerator, plasticity |
| source | Front. Neurosci. 16 (2022) 795876 |
| doi | 10.3389/fnins.2022.795876 |
| Abstract (en) | Since the beginning of information processing by electronic components, the nervous system has served as a metaphor for the organization of computational primitives. Brain-inspired computing today encompasses a class of approaches ranging from using novel nano-devices for computation to research into large-scale neuromorphic architectures, such as TrueNorth, SpiNNaker, BrainScaleS, Tianjic, and Loihi. While implementation details differ, spiking neural networks—sometimes referred to as the third generation of neural networks—are the common abstraction used to model computation with such systems. Here we describe the second generation of the BrainScaleS neuromorphic architecture, emphasizing applications enabled by this architecture. It combines a custom analog accelerator core supporting the accelerated physical emulation of bio-inspired spiking neural network primitives with a tightly coupled digital processor and a digital event-routing network.
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| bibtex | @article{pehle2022brainscales2,
author = {Pehle C, Billaudelle S, Cramer B, Kaiser J, Schreiber K, Stradmann Y, Weis J, Leibfried A, Müller E and Schemmel J},
title = {The BrainScaleS-2 Accelerated Neuromorphic System With Hybrid Plasticity},
journal = {Front. Neurosci.},
year = {2022},
volume = {16},
number = {795876},
pages = {},
month = {feb},
doi = {10.3389/fnins.2022.795876}
} |
| Datei |
