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Towards Efficient Collaborative Deep Learning Inference for Image-based Sensing Systems
- Alternative Title
- 영상 기반 센서 시스템을 위한 효율적 협력 딥러닝 추론 연구
- Author(s)
- 안정모
- Advisor
- 안정섭
- Department
- 일반대학원 인공지능학과
- Publisher
- The Graduate School, Ajou University
- Publication Year
- 2022-08
- Language
- eng
- Alternative Abstract
- Image-based sensing systems collect images in a real-life environment and analyze the images to extract meaningful context for further application-level decision making. These systems are composed of embedded devices which serve as image capturing sensing units and an inference server to analyze the collected images. This dissertation introduces methods to address the system requirements when implementing image-based sensing systems from the perspective of these different hardware components. The role of embedded devices, which possess computing and energy resource limitations, is typically to collect images and send them to the remote inference server for processing. Here, it is important to consider the battery lifetime of these constraint platforms by controlling on-board computation and communication overhead. As for the inference server, which holds enough computational power, its main role in an image-based sensing system is to apply machine learning (or other inference) models to the images collected from individual embedded devices. High inference accuracy greatly affects the application performance of image-based sensing systems. However, deep learning models that achieve high inference accuracy require a significant amount of computation. Thus, despite the computing resources of a modern server, the computational latency is not simply negligible. The high latency of operating the deep learning model can cause violations of service level objectives which negatively impacts the application service. In this dissertation, we propose methods for supporting efficient collaborative deep learning inference for image-based sensing systems to address such aforementioned challenges. First, we exemplify the system lifetime challenge that commonly occurs in image-based sensing system use cases via a wireless image sensor network (WISN) consisting of resource-constrained embedded devices. This dissertation shows that simple computations at the sensing platforms, such as data compression techniques, can significantly reduce the communication overhead, while still maintaining an acceptable classification performance. Specifically, by applying the proposed image compression scheme to a real-world scenario, animal habitat monitoring, we observe that the amount of transmission data is greatly reduced while minimizing inference accuracy degradation at the inference server. Second, we introduce a challenging application scenario which requires highly accurate inference performance with minimal processing latency. This dissertation proposes a system architecture consisting of a low latency object detection deep learning model with pre- and post-processing mechanisms to achieve the application goals. By applying such an inference process to the video-based safety monitoring system, we show that our proposed system successfully meets given a challenging set of practical (industrial) application-level requirements. Finally, considering the observations from the works introduced above, we propose an adaptive computation offloading decision scheme for collaborative inference between a mobile GPU-based embedded platform and modern deep learning servers. Specifically, we design a latency-aware collaborative execution technique by accurately identifying the load status of the deep learning server and the communication status. The works presented in this dissertation can be used as guidelines in addressing some of the core challenges in designing practically applicable image-based sensing systems, from extending individual device lifetime to simultaneously improving the inference accuracy and latency.
- Fulltext
- Appears in Collections:
- Graduate School of Ajou University > Department of Artificial Intelligence > 4. Theses(Ph.D)
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