Publication Type
Conference Proceeding Article
Book Title/Conference/Journal
AIChallengeIoT'19: Proceedings of the First International Workshop on Challenges in Artificial Intelligence and Machine Learning for Internet of Things, New York, 2019 November 10-13
Year
11-2019
Abstract
Edge accelerator is a class of brand-new purpose-built System On a Chip (SoC) for running deep learning models efficiently on edge devices. These accelerators offer various benefits such as ultra-low latency, sensitive data protection, and high availability due to their locality and are opening up interminable opportunities for building sensory systems in the real world. Naturally, in the context of sensory awareness systems, e.g., IoT, wearables, and other sensory devices, the emergence of edge accelerators is pushing us to rethink how we design these systems at a personal-scale. To this end, in this paper we take a closer look at the performance of a set of edge accelerators in running a collection of personal-scale sensory deep learning models. We benchmark eight different models with varying architectures and tasks (i.e., motion, audio, and vision) across seven platform configurations with three different accelerators including Google Coral, NVidia Jetson Nano, and Intel Neural Compute Stick. We report on their execution performance concerning latency, memory, and power consumption while discussing their current workflows and limitations. The results and insights lay an empirical foundation for the development of sensory systems on edge accelerators.
Keywords
resource characterisation, edge accelerators, sensing models
Disciplines
Artificial Intelligence and Robotics
Subject(s)
Applied or Integration/Application Scholarship
ISSN/ISBN
9781450370134
Publisher
ACM
DOI
10.1145/3363347.3363363
Version
publishedVersion
Language
eng
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Format
application/PDF
Citation
ANTONINI, Mattia; VU, Tran Huy; MIN, Chulhong; MONTANARI, Alessandro; MATHUR, Akhil; and KAWSAR, Fahim.
Resource characterisation of personal-scale sensing models on edge accelerators. (2019). AIChallengeIoT'19: Proceedings of the First International Workshop on Challenges in Artificial Intelligence and Machine Learning for Internet of Things, New York, 2019 November 10-13. 49-55.
Available at: https://ink.library.smu.edu.sg/studentpub/13
Additional URL
https://doi.org/10.1145/3363347.3363363
