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The BrainScaleS-2 accelerated neuromorphic system with hybrid plasticity

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arxiv 2201.11063 v2 pith:V3EIRO4I submitted 2022-01-26 cs.NE cond-mat.dis-nnq-bio.NC

classification cs.NEcond-mat.dis-nnq-bio.NC
keywords neuralneuromorphicacceleratedarchitecturebrainscalescomputationdigitalgeneration
verification ladder T0 review T1 audit T2 compute T3 formal
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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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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. High-Speed Time Series Prediction with a GHz-rate Photonic Spiking Neural Network built with a single VCSEL

    physics.comp-ph 2024-12 conditional novelty 4.0 of 10

    A single VCSEL laser, split into hundreds of virtual spiking neurons and fed with a ten-step-delayed copy of the input, predicts the chaotic Mackey-Glass series with NMSE as low as 0.051.

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