Multi-Stage Model Compression and Commercialization for IoT-enabled Edge Cameras

• Main Authors: Jen-Wei Wang, 王仁緯
• Other Authors: Ching-Hu Lu
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/r979xa

碩士 === 國立臺灣科技大學 === 電機工程系 === 106 === For the upcoming IoT era, a plethora of data will be generated from IoT-enabled devices. If all of data completely rely on traditional cloud servers to process, the cloud servers and the bandwidth will eventually be overwhelmed. To reduce computing bottlenecks on cloud servers, the demand for edge computing is quickly increasing. With the advances in deep neural network (DNN), wide-area monitoring through a large number of edge cameras has been extensively used in various smart-living scenarios. However, most existing edge cameras cannot successfully run a DNN model due to their inadequate memory sizes for a DNN model. Even if a DNN model can be run, there is no corresponding solution of customizable and sharable business model to leverage DNN models as C2M IoT-as-a-Service (customer-to-machine Internet of Things as a Service). To address above two issues, this study first proposes multi-stage model compression, which combines both parameter and structure compression. The proposed method has multiple-compression stages to minimize model size while maintaining as much accuracy as possible. To commercialize the compressed DNN models, our study further proposed BC (blockchain)-enabled model commercialization which features dynamic task-driven and sharable model deployment on edge cameras. The whole system was built upon a blockchain using smart contracts to meet the needs of various customers for wide-area monitoring applications. Even with sharable DNN models, each customer can obtain custmizable event-driven notifications. In the evaluation, through the multi-stage model compression, the size of a DNN model can be reduced by 55 times at the cost of only 5% decrease in the accuracy, which outperforms the state-of-the-art literature. In addition, through the blockchain-based model commercialization, its prototype for wide area video-surveillance was implemented to verify feasibility. Through a friendly web-enabled interface, a user can fill in requests, which will be parsed to match an appropriate DNN model in the model repository. After charging the customer through the cryptocurrency of Ethereum, the system will automatically generate an Ethereum smart contract and dynamically deploy The associated DNN models for the task on their corresponding edge cameras. Other customers applying for a similar request can share the deployed models. Even with the sharable the models, the system can provide customizable event-driven notifications for each customer.