| Online-Ressource |
Verfasst von: | Stradmann, Yannik [VerfasserIn]  |
| Billaudelle, Sebastian [VerfasserIn]  |
| Breitwieser, Oliver [VerfasserIn]  |
| Ebert, Falk [VerfasserIn]  |
| Emmel, Arne [VerfasserIn]  |
| Husmann, Dan [VerfasserIn]  |
| Ilmberger, Joscha [VerfasserIn]  |
| Müller, Eric [VerfasserIn]  |
| Spilger, Philipp [VerfasserIn]  |
| Weis, Johannes [VerfasserIn]  |
| Schemmel, Johannes [VerfasserIn]  |
Titel: | Demonstrating analog inference on the BrainScaleS-2 mobile system |
Verf.angabe: | Yannik Stradmann, Sebastian Billaudelle, Oliver Breitwieser, Falk Leonard Ebert, Arne Emmel, Dan Husmann, Joscha Ilmberger, Eric Müller, Philipp Spilger, Johannes Weis, and Johannes Schemmel (Member, IEEE) |
E-Jahr: | 2022 |
Jahr: | 21 September 2022 |
Umfang: | 11 S. |
Fussnoten: | "Date of current version 24 October 2022" ; Gesehen am 29.09.2022 |
Titel Quelle: | Enthalten in: IEEE open journal of circuits and systems |
Ort Quelle: | New York, NY : IEEE, 2020 |
Jahr Quelle: | 2022 |
Band/Heft Quelle: | 3(2022), Seite 252-262 |
ISSN Quelle: | 2644-1225 |
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 192 μJ for the ASIC and achieve a classification time of 276 μs per electrocardiographic patient sample. Patients with atrial fibrillation are correctly identified with a detection rate of (93.7 ± 0.7)% at (14.0 ± 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. |
DOI: | doi:10.1109/OJCAS.2022.3208413 |
URL: | kostenfrei: Volltext: https://doi.org/10.1109/OJCAS.2022.3208413 |
| kostenfrei: Volltext: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9896927 |
| DOI: https://doi.org/10.1109/OJCAS.2022.3208413 |
Datenträger: | Online-Ressource |
Sprache: | eng |
Sach-SW: | accelerator |
| analog computing |
| Central Processing Unit |
| convolutional deep neural networks |
| electrocardiography |
| Field programmable gate arrays |
| inference |
| low-power |
| medical |
| neuromorphic |
| Neuromorphics |
| Neurons |
| Random access memory |
| Synapses |
| Virtual machine monitors |
K10plus-PPN: | 1817798316 |
Verknüpfungen: | → Zeitschrift |
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Lokale URL UB: | Zum Volltext |
Demonstrating analog inference on the BrainScaleS-2 mobile system / Stradmann, Yannik [VerfasserIn]; 21 September 2022 (Online-Ressource)