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- W3017144562 abstract "Smartwatches have improved significantly in terms of the processing resources and are a potential candidate for using deep learning for applications relying on a host of supported sensors. However, the tradeoff between running a deep neural network on the smartwatch verses sending the sensor data to the back-end server for processing is not well understood. This paper presents a case study of these tradeoffs for a Samsung S3 smartwatch in the context of a word recognition task using a Convolutional Neural Network (CNN) based on Google's Speech Command data set. Tensorflow.js was used to do edge inference on the watch and under various computational offloading conditions using 2G, Edge, 3G and WiFi profiles. The results are that running a CNN on the watch is superior in terms of memory, CPU, inference time, and battery depletion. In specific, the inference time was 3 times longer for 2G and 1.6 times longer for WiFi with computational offloading. In addition, the battery depletion rate was about the same when using 2G or Edge Lossy but was 60-70% worse when using 3G or WiFi for computational offloading." @default.
- W3017144562 created "2020-04-24" @default.
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- W3017144562 date "2020-02-01" @default.
- W3017144562 modified "2023-09-27" @default.
- W3017144562 title "Computational Offloading of Convolutional Neural Network on a Smart Watch" @default.
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- W3017144562 doi "https://doi.org/10.1109/icaiic48513.2020.9064970" @default.
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