TY - GEN
T1 - Software Engineering Approaches for TinyML based IoT Embedded Vision
T2 - 4th IEEE/ACM International Workshop on Software Engineering Research and Practice for the IoT, SERP4IoT 2022
AU - Lakshman, Shashank Bangalore
AU - Eisty, Nasir U.
N1 - Publisher Copyright:
© 2022 ACM.
PY - 2022
Y1 - 2022
N2 - Internet of Things (IoT) has catapulted human ability to control our environments through ubiquitous sensing, communication, computation, and actuation. Over the past few years, IoT has joined forces with Machine Learning (ML) to embed deep intelligence at the far edge. TinyML (Tiny Machine Learning) has enabled the deployment of ML models for embedded vision on extremely lean edge hardware, bringing the power of IoT and ML together. However, TinyML powered embedded vision applications are still in a nascent stage, and they are just starting to scale to widespread real-world IoT deployment. To harness the true potential of IoT and ML, it is necessary to provide product developers with robust, easy-to-use software engineering (SE) frameworks and best practices that are customized for the unique challenges faced in TinyML engineering. Through this systematic literature review, we aggregated the key challenges reported by TinyML developers and identified state-of-art SE approaches in large-scale Computer Vision, Machine Learning, and Embedded Systems that can help address key challenges in TinyML based IoT embedded vision. In summary, our study draws synergies between SE expertise that embedded systems developers and ML developers have independently developed to help address the unique challenges in the engineering of TinyML based IoT embedded vision.
AB - Internet of Things (IoT) has catapulted human ability to control our environments through ubiquitous sensing, communication, computation, and actuation. Over the past few years, IoT has joined forces with Machine Learning (ML) to embed deep intelligence at the far edge. TinyML (Tiny Machine Learning) has enabled the deployment of ML models for embedded vision on extremely lean edge hardware, bringing the power of IoT and ML together. However, TinyML powered embedded vision applications are still in a nascent stage, and they are just starting to scale to widespread real-world IoT deployment. To harness the true potential of IoT and ML, it is necessary to provide product developers with robust, easy-to-use software engineering (SE) frameworks and best practices that are customized for the unique challenges faced in TinyML engineering. Through this systematic literature review, we aggregated the key challenges reported by TinyML developers and identified state-of-art SE approaches in large-scale Computer Vision, Machine Learning, and Embedded Systems that can help address key challenges in TinyML based IoT embedded vision. In summary, our study draws synergies between SE expertise that embedded systems developers and ML developers have independently developed to help address the unique challenges in the engineering of TinyML based IoT embedded vision.
KW - Embedded vision
KW - Iot
KW - Software engineering
KW - Systematic literature review
KW - Tinyml
UR - http://www.scopus.com/inward/record.url?scp=85136221574&partnerID=8YFLogxK
U2 - 10.1145/3528227.3528569
DO - 10.1145/3528227.3528569
M3 - Conference contribution
AN - SCOPUS:85136221574
T3 - Proceedings - 4th International Workshop on Software Engineering Research and Practice for the IoT, SERP4IoT 2022
SP - 33
EP - 40
BT - Proceedings - 4th International Workshop on Software Engineering Research and Practice for the IoT, SERP4IoT 2022
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 19 May 2022
ER -