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cDeepArch: A Compact Deep Neural Network Architecture for Mobile Sensing
- Source :
- SECON
- Publication Year :
- 2019
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2019.
-
Abstract
- Mobile sensing is a promising sensing paradigm in the era of Internet of Things (IoT) that utilizes mobile device sensors to collect sensory data about sensing targets and further applies learning techniques to recognize the sensed targets to correct classes or categories. Due to the recent great success of deep learning, an emerging trend is to adopt deep learning in this recognition process, while we find an overlooked yet crucial issue to be solved in this paper — The size of deep learning models should be sufficiently large for reliably classifying various types of recognition targets, while the achieved processing delay may fail to satisfy the stringent latency requirement from applications. If we blindly shrink the deep learning model for acceleration, the performance cannot be guaranteed. To cope with this challenge, this paper presents a compact deep neural network architecture, namely cDeepArch . The key idea of the cDeepArch design is to decompose the entire recognition task into two lightweight sub-problems: context recognition and the context-oriented target recognitions. This decomposition essentially utilizes the adequate storage to trade for the CPU and memory resource consumptions during execution. In addition, we further formulate the execution latency for decomposed deep learning models and propose a set of enhancement techniques, so that system performance and resource consumption can be quantitatively balanced. We implement a cDeepArch prototype system and conduct extensive experiments. The result shows that cDeepArch achieves excellent recognition performance and the execution latency is also lightweight.
- Subjects :
- Context model
Artificial neural network
Computer Networks and Communications
Computer science
business.industry
Deep learning
Distributed computing
020206 networking & telecommunications
02 engineering and technology
010501 environmental sciences
01 natural sciences
Computer Science Applications
Data modeling
Task analysis
Neural network architecture
0202 electrical engineering, electronic engineering, information engineering
Central processing unit
Artificial intelligence
Latency (engineering)
Electrical and Electronic Engineering
business
Mobile device
Processing delay
Software
0105 earth and related environmental sciences
Subjects
Details
- ISSN :
- 15582566 and 10636692
- Volume :
- 27
- Database :
- OpenAIRE
- Journal :
- IEEE/ACM Transactions on Networking
- Accession number :
- edsair.doi.dedup.....5ab0e7c1c508c2a4364d7ff42398520d