dc.contributor.author | Sudharsan, Bharath | |
dc.contributor.author | Patel, Pankesh | |
dc.contributor.author | Wahid, Abdul | |
dc.contributor.author | Yahya, Muhammad | |
dc.contributor.author | Breslin, John G. | |
dc.contributor.author | Ali, Muhammad Intizar | |
dc.date.accessioned | 2021-05-21T10:26:16Z | |
dc.date.available | 2021-05-21T10:26:16Z | |
dc.date.issued | 2021-05-18 | |
dc.identifier.citation | Sudharsan, Bharath, Patel, Pankesh, Wahid, Abdul, Yahya, Muhammad, Breslin, John G., & Ali, Muhammad Intizar. (2021). Porting and Execution of Anomalies Detection Models on Embedded Systems in IoT: Demo abstract. Paper presented at the Proceedings of the International Conference on Internet-of-Things Design and Implementation, Charlottesvle, VA, USA, 18-21 May, https://doi.org/10.1145/3450268.3453513 | en_IE |
dc.identifier.uri | http://hdl.handle.net/10379/16776 | |
dc.description.abstract | In the Industry 4.0 era, Microcontrollers (MCUs) based tiny embedded sensor systems have become the sensing paradigm to interact with the physical world. In 2020, 25.6 billion MCUs were
shipped, and over 250 billion MCUs are already operating in the
wild. Such low-power, low-cost MCUs are being used as the brain to
control diverse applications and soon will become the global digital
nervous system. In an Industrial IoT setup, such tiny MCU-based
embedded systems are equipped with anomaly detection models
and mounted on production plant machines for monitoring the
machine’s health/condition. These models process the machine’s
health data (from temperature, RPM, vibration sensors) and raise
timely alerts when it predicts/detects data patterns that show deviations from the normal operation state.
In this demo, we train One Class Support Vector Machines (OCSVM) based anomaly detection models and port the trained models
to their MCU executable versions. We then deploy and execute
the ported models on 4 popular MCUs and report their on-board
inference performance along with their memory (Flash and SRAM)
consumption. The steps/procedure that we show in the demo is
generic, and the viewers can use it to efficiently port a wide variety of datasets-trained classifiers and execute them on different
resource-constrained MCU and small CPU-based devices. | en_IE |
dc.description.sponsorship | This publication has emanated from research supported in part by a research grant from Science Foundation Ireland (SFI) under Grant Number
SFI/16/RC/3918 (Confirm) and also by a research grant from Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289_P2 (Insight), with
both grants co-funded by the European Regional Development Fund. | en_IE |
dc.format | application/pdf | en_IE |
dc.language.iso | en | en_IE |
dc.publisher | Association for Computing Machinery (ACM) | en_IE |
dc.relation.ispartof | IoTDI '21: Proceedings of the International Conference on Internet-of-Things Design and Implementation | en |
dc.rights | Attribution 4.0 International (CC BY 4.0) | |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
dc.subject | Offline Inference | en_IE |
dc.subject | Intelligent Embedded Systems | en_IE |
dc.subject | Edge AI | en_IE |
dc.title | Porting and execution of anomalies detection models on embedded systems in IoT: Demo abstract | en_IE |
dc.type | Demonstration paper | en_IE |
dc.date.updated | 2021-05-21T09:37:17Z | |
dc.identifier.doi | 10.1145/3450268.3453513 | |
dc.local.publishedsource | https://doi.org/10.1145/3450268.3453513 | en_IE |
dc.description.peer-reviewed | peer-reviewed | |
dc.contributor.funder | Science Foundation Ireland | en_IE |
dc.contributor.funder | European Regional Development Fund | en_IE |
dc.internal.rssid | 25999428 | |
dc.local.contact | Bharath Sudharsan, Insight Centre For Data Analytics, Nui Galway. Email: b.sudharsan1@nuigalway.ie | |
dc.local.copyrightchecked | Yes | |
dcterms.project | info:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2289/IE/INSIGHT - Irelands Big Data and Analytics Research Centre/ | en_IE |
nui.item.downloads | 126 | |