Demonstrating Analog Inference on the BrainScaleS-2 Mobile System

• URL: http://arxiv.org/abs/2103.15960v1
• Date: Mon, 29 Mar 2021 21:22:15 GMT
• Title: Demonstrating Analog Inference on the BrainScaleS-2 Mobile System
• Authors: Yannik Stradmann, Sebastian Billaudelle, Oliver Breitwieser, Falk Leonard Ebert, Arne Emmel, Dan Husmann, Joscha Ilmberger, Eric M\"uller, Philipp Spilger, Johannes Weis, Johannes Schemmel
• Abstract summary: We present the BrainScaleS-2 mobile system as a compact analog inference engine based on the BrainScaleS-2 ASIC. We demonstrate its capabilities at classifying a medical electrocardiogram dataset. The system is directly applicable to edge inference applications due to its small size, power envelope and flexible I/O capabilities.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present the BrainScaleS-2 mobile system as a compact analog inference engine based on the BrainScaleS-2 ASIC and demonstrate its capabilities at classifying a medical electrocardiogram dataset. The analog network core of the ASIC is utilized to perform the multiply-accumulate operations of a convolutional deep neural network. We measure a total energy consumption of 192uJ for the ASIC and achieve a classification time of 276us per electrocardiographic patient sample. Patients with atrial fibrillation are correctly identified with a detection rate of 93.7(7)% at 14.0(10)% false positives. The system is directly applicable to edge inference applications due to its small size, power envelope and flexible I/O capabilities. Possible future applications can furthermore combine conventional machine learning layers with online-learning in spiking neural networks on a single BrainScaleS-2 ASIC. The system has successfully participated and proven to operate reliably in the independently judged competition "Pilotinnovationswettbewerb 'Energieeffizientes KI-System'" of the German Federal Ministry of Education and Research (BMBF).

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