RAP: A Software Framework of Developing Convolutional Neural Networks for Resource-constrained Devices Using Environmental Monitoring as a Case Study
| العنوان: | RAP: A Software Framework of Developing Convolutional Neural Networks for Resource-constrained Devices Using Environmental Monitoring as a Case Study |
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| المؤلفون: | Chia-Heng Tu, Hsiao-Hsuan Chang, Qihui Sun |
| المصدر: | ACM Transactions on Cyber-Physical Systems. 5:1-28 |
| بيانات النشر: | Association for Computing Machinery (ACM), 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | Control and Optimization, Artificial neural network, Computer Networks and Communications, Computer science, business.industry, Deep learning, media_common.quotation_subject, Cyber-physical system, computer.file_format, Python (programming language), computer.software_genre, Convolutional neural network, Human-Computer Interaction, Software framework, Computer architecture, Debugging, Artificial Intelligence, Hardware and Architecture, Artificial intelligence, Executable, business, computer, media_common, computer.programming_language |
| الوصف: | Monitoring environmental conditions is an important application of cyber-physical systems. Typically, the monitoring is to perceive surrounding environments with battery-powered, tiny devices deployed in the field. While deep learning-based methods, especially the convolutional neural networks (CNNs), are promising approaches to enriching the functionalities offered by the tiny devices, they demand more computation and memory resources, which makes these methods difficult to be adopted on such devices. In this article, we develop a software framework, RAP , that permits the construction of the CNN designs by aggregating the existing, lightweight CNN layers, which are able to fit in the limited memory (e.g., several KBs of SRAM) on the resource-constrained devices satisfying application-specific timing constrains. RAP leverages the Python-based neural network framework Chainer to build the CNNs by mounting the C/C++ implementations of the lightweight layers, trains the built CNN models as the ordinary model-training procedure in Chainer, and generates the C version codes of the trained models. The generated programs are compiled into target machine executables for the on-device inferences. With the vigorous development of lightweight CNNs, such as binarized neural networks with binary weights and activations, RAP facilitates the model building process for the resource-constrained devices by allowing them to alter, debug, and evaluate the CNN designs over the C/C++ implementation of the lightweight CNN layers. We have prototyped the RAP framework and built two environmental monitoring applications for protecting endangered species using image- and acoustic-based monitoring methods. Our results show that the built model consumes less than 0.5 KB of SRAM for buffering the runtime data required by the model inference while achieving up to 93% of accuracy for the acoustic monitoring with less than one second of inference time on the TI 16-bit microcontroller platform. |
| تدمد: | 2378-9638 2378-962X |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_________::6379aeef07698cc93c3d6d034fd12e42Test https://doi.org/10.1145/3472612Test |
| رقم الانضمام: | edsair.doi...........6379aeef07698cc93c3d6d034fd12e42 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 23789638 2378962X |
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