| Autor: |
Stradmann, Yannik, Billaudelle, Sebastian, Breitwieser, Oliver, Ebert, Falk Leonard, Emmel, Arne, Husmann, Dan, Ilmberger, Joscha, Müller, Eric, Spilger, Philipp, Weis, Johannes, Schemmel, Johannes |
| Rok vydání: |
2021 |
| Předmět: |
|
| Zdroj: |
in IEEE Open Journal of Circuits and Systems, vol. 3, pp. 252-262, 2022 |
| Druh dokumentu: |
Working Paper |
| DOI: |
10.1109/OJCAS.2022.3208413 |
| Popis: |
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. |
| Databáze: |
arXiv |
| Externí odkaz: |
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