Towards Large-scale Network Emulation on Analog Neuromorphic Hardware

• URL: http://arxiv.org/abs/2401.16840v1
• Date: Tue, 30 Jan 2024 09:27:05 GMT
• Title: Towards Large-scale Network Emulation on Analog Neuromorphic Hardware
• Authors: Elias Arnold, Philipp Spilger, Jan V. Straub, Eric M\"uller, Dominik Dold, Gabriele Meoni, Johannes Schemmel
• Abstract summary: We present a novel software feature for the BrainScaleS-2 accelerated neuromorphic platform that facilitates the emulation of partitioned large-scale spiking neural networks. We demonstrate the training of two deep spiking neural network models, using the MNIST and EuroSAT datasets, that exceed the physical size constraints of a single-chip BrainScaleS-2 system.
• Score: 3.3535745719000087
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a novel software feature for the BrainScaleS-2 accelerated neuromorphic platform that facilitates the emulation of partitioned large-scale spiking neural networks. This approach is well suited for many deep spiking neural networks, where the constraint of the largest recurrent subnetwork fitting on the substrate or the limited fan-in of neurons is often not a limitation in practice. We demonstrate the training of two deep spiking neural network models, using the MNIST and EuroSAT datasets, that exceed the physical size constraints of a single-chip BrainScaleS-2 system. The ability to emulate and train networks larger than the substrate provides a pathway for accurate performance evaluation in planned or scaled systems, ultimately advancing the development and understanding of large-scale models and neuromorphic computing architectures.

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