MobiRNN

Author:

Cao Qingqing1,Balasubramanian Niranjan1,Balasubramanian Aruna1

Affiliation:

1. Stony Brook University, Stony Brook, NY, USA

Publisher

ACM Press

Reference18 articles.

1. Cuda. https://developer.nvidia.com/cuda-downloads.

2. Android. Hardware acceleration. https://developer.android.com/guide/topics/graphics/hardware-accel.html. accessed 8-April-2017.

3. Android. Renderscript. https://developer.android.com/guide/topics/renderscript/compute.html. accessed 8-April-2017.

4. D. Anguita, A. Ghio, L. Oneto, X. Parra, and J. L. Reyes-Ortiz. A public domain dataset for human activity recognition using smartphones. In ESANN, 2013.

5. J. Appleyard, T. Kocisky, and P. Blunsom. Optimizing Performance of Recurrent Neural Networks on GPUs. ArXiv e-prints, Apr. 2016.

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2. Building Lightweight Deep learning Models with TensorFlow Lite for Human Activity Recognition on Mobile Devices;Annals of Telecommunications;2023-07-15

3. Boosting DNN Cold Inference on Edge Devices;Proceedings of the 21st Annual International Conference on Mobile Systems, Applications and Services;2023-06-18

4. On-Device Deep Learning for Mobile and Wearable Sensing Applications: A Review;IEEE Sensors Journal;2023-03-15

5. Transforming Large-Size to Lightweight Deep Neural Networks for IoT Applications;ACM Computing Surveys;2023-02-09

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