dc.contributor.author | Sudharsan, Bharath | |
dc.contributor.author | Patel, Pankesh | |
dc.contributor.author | Breslin, John G. | |
dc.contributor.author | Ali, Muhammad Intizar | |
dc.date.accessioned | 2021-05-21T09:52:30Z | |
dc.date.available | 2021-05-21T09:52:30Z | |
dc.date.issued | 2021-05-19 | |
dc.identifier.citation | Sudharsan, Bharath, Patel, Pankesh, Breslin, John G., & Ali, Muhammad Intizar. (2021). SRAM optimized porting and execution of machine learning classifiers on MCU-based IoT devices: Demo abstract. Paper presented at the Proceedings of the ACM/IEEE 12th International Conference on Cyber-Physical Systems, Nashville, Tennessee, 19-21 May, https://doi.org/10.1145/3450267.3451999 | en_IE |
dc.identifier.uri | http://hdl.handle.net/10379/16775 | |
dc.description.abstract | With the introduction of edge analytics, IoT devices are becoming smarter and ready for AI applications. However, any increase in the training data results in a linear increase in the space complexity of the trained Machine Learning (ML) models, which means they cannot be deployed on IoT devices that have limited memory. To alleviate such memory issues, we recently proposed an SRAM-optimized classifier porting, stitching, and efficient deployment method in [3]. This is currently the most resource-friendly approach that enables large classifiers to be comfortably executed on microcontroller unit (MCU) based IoT devices, and perform ultra-fast classifications (1--4x times faster than state-of-the-art libraries) while consuming 0 bytes of SRAM.
In this demo, realizing our recent SRAM-optimized approach, we port and execute 7 dataset-trained classifiers on 7 popular MCUs, and report their inference performance. It is apparent from the demo results that realizing our approach makes even the slowest Atmega328P MCU perform faster unit inference than a NVIDIA Jetson Nano GPU and Raspberry Pi 4 CPU. | 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 | ICCPS '21: Proceedings of the ACM/IEEE 12th International Conference on Cyber-Physical Systems | 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 MCUs | en_IE |
dc.subject | Edge AI | en_IE |
dc.subject | SRAM Optimization | en_IE |
dc.title | SRAM optimized porting and execution of machine learning classifiers on MCU-based IoT devices: Demo abstract | en_IE |
dc.type | Conference Paper | en_IE |
dc.date.updated | 2021-05-21T09:33:02Z | |
dc.identifier.doi | 10.1145/3450267.3451999 | |
dc.local.publishedsource | https://doi.org/10.1145/3450267.3451999 | 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 | 25999426 | |
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 | 152 | |