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