Demonstrating Analog Inference on the BrainScaleS-2 Mobile System
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arXiv
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| Autori principali: | , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2021
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| author | Stradmann, Yannik Billaudelle, Sebastian Breitwieser, Oliver Ebert, Falk Leonard Emmel, Arne Husmann, Dan Ilmberger, Joscha Müller, Eric Spilger, Philipp Weis, Johannes Schemmel, Johannes |
| author_facet | Stradmann, Yannik Billaudelle, Sebastian Breitwieser, Oliver Ebert, Falk Leonard Emmel, Arne Husmann, Dan Ilmberger, Joscha Müller, Eric Spilger, Philipp Weis, Johannes Schemmel, Johannes |
| contents | 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}$1.0)% false positives. The system is directly applicable to edge inference applications due to its small size, power envelope, and flexible I/O capabilities. It has enabled the BrainScaleS-2 ASIC to be operated reliably outside a specialized lab setting. In future applications, the system allows for a combination of conventional machine learning layers with online learning in spiking neural networks on a single neuromorphic platform. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2103_15960 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Demonstrating Analog Inference on the BrainScaleS-2 Mobile System Stradmann, Yannik Billaudelle, Sebastian Breitwieser, Oliver Ebert, Falk Leonard Emmel, Arne Husmann, Dan Ilmberger, Joscha Müller, Eric Spilger, Philipp Weis, Johannes Schemmel, Johannes Hardware Architecture Machine Learning Neural and Evolutionary Computing We present the BrainScaleS-2 mobile system as a compact analog inference engine based on the BrainScaleS-2 ASIC and demonstrate its capabilities at classifying a medical electrocardiogram dataset. The analog network core of the ASIC is utilized to perform the multiply-accumulate operations of a convolutional deep neural network. At a system power consumption of 5.6W, we measure a total energy consumption of 192uJ for the ASIC and achieve a classification time of 276us per electrocardiographic patient sample. Patients with atrial fibrillation are correctly identified with a detection rate of (93.7${\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. |
| title | Demonstrating Analog Inference on the BrainScaleS-2 Mobile System |
| topic | Hardware Architecture Machine Learning Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2103.15960 |