Abstract
Machine learning applications on resource-constrained devices such as microcontroller units often use models trained externally on more powerful devices. This approach, however, limits a later adaptation of the machine learning model in the device to changing data. Differently, on-device training allows the model to be updated for new datasets, but the training process needs to take into account the resource limitations of the device. This paper compares on-device training performance for a keyword spotting task using two popular microcontroller boards, Arduino Nano 33 BLE Sense and Arduino Portenta H7, in terms of inference accuracy, training latency, and current consumption. We use feedforward neural networks having a single hidden layer for models. The inference accuracy has been significantly improved using the Portenta H7 board by employing more neurons fitted to its memory budget, compared to the Nano board. With a neural network having 25 neurons for a hidden layer, the 5.0 x inference and 4.2 x training speedups are achieved using the Arduino Portenta H7 board, compared to the Arduino Nano 33 BLE Sense. While the memory of the Arduino Nano 33 BLE Sense is capable to train a neural network for the keyword spotting task, the Arduino Portenta H7 gives new possibilities for exploring more complex models for more complex problems thanks to a larger memory budget and adapting a model to new data in lower latency.
Original language | English |
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Title of host publication | 2022 11th Mediterranean Conference on Embedded Computing (MECO 2022): Proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Number of pages | 4 |
ISBN (Electronic) | 9781665468282 |
DOIs | |
Publication status | Published - 21 Jun 2022 |
Event | 11th Mediterranean Conference on Embedded Computing, MECO 2022 - Budva, Montenegro Duration: 07 Jun 2022 → 10 Jun 2022 |
Publication series
Name | 11th Mediterranean Conference on Embedded Computing: Proceedings |
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Publisher | IEEE |
ISSN (Print) | 2377-5475 |
ISSN (Electronic) | 2637-9511 |
Conference
Conference | 11th Mediterranean Conference on Embedded Computing, MECO 2022 |
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Country/Territory | Montenegro |
City | Budva |
Period | 07/06/2022 → 10/06/2022 |
Bibliographical note
Funding Information:ACKNOWLEDGMENT This work was partially supported by the Spanish Government under contracts PID2019-106774RB-C21, PCI2019-111850-2 (DiPET CHIST-ERA CHIST-ERA-SDCDN-002), PCI2019-111851-2 (LeadingEdge CHIST-ERA), and UK Engineering and Physical Sciences Research Council (EP/T022345/1).
Publisher Copyright:
© 2022 IEEE.
Keywords
- IoT
- machine learning
- TinyML
ASJC Scopus subject areas
- Instrumentation
- Computer Networks and Communications
- Computer Science Applications
- Health Informatics