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arXiv · 2103.15960

Demonstrating Analog Inference on the BrainScaleS-2 Mobile System

Yannik Stradmann·Sebastian Billaudelle·Oliver Breitwieser·Falk Leonard Ebert·Arne Emmel·Dan Husmann·Joscha Ilmberger·Eric Müller·Philipp Spilger·Johannes Weis·Johannes Schemmel
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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. At a system power consumption of 5.6W, 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${\pm}$0.7)% at (14.0${\pm}$1.0)% false positives. The system is directly applicable to edge inference applications due to its small size, power envelope, and flexible I/O capabilities. It has enabled the BrainScaleS-2 ASIC to be operated reliably outside a specialized lab setting. In future applications, the system allows for a combination of conventional machine learning layers with online learning in spiking neural networks on a single neuromorphic platform.

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Yannik Stradmann, Sebastian Billaudelle, Oliver Breitwieser, Falk Leonard Ebert, Arne Emmel, Dan Husmann, Joscha Ilmberger, Eric Müller, Philipp Spilger, Johannes Weis, Johannes Schemmel. 2022-10-27. Demonstrating Analog Inference on the BrainScaleS-2 Mobile System. https://doi.org/10.1109/ojcas.2022.3208413

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