As we enter the Internet of Things (IoT) era, the
size of mobile computing devices is largely reduced while their
computing capability is dramatically improved. Meanwhile,
machine learning technologies have been well developed and
shown cutting edge performance in various tasks, leading to
their wide adoption. As a result, moving machine learning,
especially deep learning capability to the edge of the IoT
is a trend happening today. But directly moving machine
learning algorithms which originally run on PC platform is not
feasible for IoT devices due to their relatively limited computing
power. In this paper, we first reviewed several representative
approaches for enabling deep learning on mobile/IoT devices.
Then we evaluated the performance and impact of these
methods on IoT platform equipped with integrated GPU and
ARM processor. Our results show that we can enable the deep
learning capability on the edge of the IoT if we apply these
approaches in an efficient manner.
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Edge AI: Systems Design and ML for IoT Data Analytics
With the explosion in Big Data, it is often forgotten that much of the data nowadays is generated at the edge. Specifically, a major source of data is users' endpoint devices like phones, smart watches, etc., that are connected to the internet, also known as the Internet-of-Things (IoT). This "edge of data" faces several new challenges related to hardware-constraints, privacy-aware learning, and distributed learning (both training as well as inference). So what systems and machine learning algorithms can we use to generate or exploit data at the edge? Can network science help us solve machine learning (ML) problems? Can IoT-devices help people who live with some form of disability and many others benefit from health monitoring?
In this tutorial, we introduce the network science and ML techniques relevant to edge computing, discuss systems for ML (e.g., model compression, quantization, HW/SW co-design, etc.) and ML for systems design (e.g., run-time resource optimization, power management for training and inference on edge devices), and illustrate their impact in addressing concrete IoT applications.
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- Award ID(s):
- 2114499
- NSF-PAR ID:
- 10259933
- Date Published:
- Journal Name:
- 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
- Page Range / eLocation ID:
- 3565 to 3566
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3394486.3406479
