參考文獻 |
[1] A. Krizhevsky, I. Sutskever, G. Hinton, "Imagenet classification with deep convolutional neural networks", Paper presented at the Advances in neural information processing systems, pp. 1097-1105, 2012.
[2] A. Graves, A. Mohamed, G. Hinton, " Speech recognition with deep recurrent neural networks", Paper presented at the Acoustics, speech and signal processing (icassp), pp. 6645-6649, 2013.
[3] N. Lane, S. Bhattacharya, A. Mathur, P. Georgiev, C. Forlivesi, F. Kawsar, " Squeezing deep learning into mobile and embedded devices", IEEE Pervasive Computing, no. 3, pp. 82-88, 2017.
[4] NVIDIA. (2018). 嵌入式系統開發套件、模組及SDK | NVIDIA Jetson. from https://www.nvidia.com/zh-tw/autonomous-machines/embedded-systems-dev-kits-modules/
[5] ARM. (2018). Project Trillium - Arm. from https://www.arm.com/products/silicon-ip-cpu/machine-learning/project-trillium
[6] S. Han, H. Mao, W. Dally, "Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding", arXiv preprint arXiv:1510.00149, 2015.
[7] S. Bhattacharya, N. D. Lane, “Sparsification and Separation of Deep Learning Layers for Constrained Resource Inference on Wearables”, Paper presented at the Proceedings of the 14th ACM Conference on Embedded Network Sensor Systems CD-ROM, Stanford, CA, USA, 2016.
[8] A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, H. Adam, “Mobilenets: Efficient convolutional neural networks for mobile vision applications”, arXiv preprint arXiv:1704.04861, 2017.
[9] L. Lai, N. Suda, V. Chandra, “CMSIS-NN: Efficient Neural Network Kernels for Arm Cortex-M CPUs”, eprint arXiv:1801.06601, 2018.
[10] Y. Zhang, N. Suda, L. Lai, V. Chandra, “Hello edge: Keyword spotting on microcontrollers”, arXiv preprint arXiv:1711.07128 ,2017
[11] J.-w. Chen, C.-H. Liu, Y.-F. Liao, “基於深層類神經網路之音訊事件偵測系統” (Deep Neural Networks for Audio Event Detection) [In Chinese]. Paper presented at the Proceedings of the 28th Conference on Computational Linguistics and Speech Processing, 2016.
[12] CS231n, Stanford. (2018). Convolutional Neural Networks for Visual Recognition. from http://cs231n.github.io/convolutional-networks/
[13] C.-S. Li, (2018). Depthwise Separable Convolution. from http://blog.yeshuanova.com/blog/posts/depthwise-separable-convolution/
[14] I. Hubara, M. Courbariaux, D. Soudry, E.-Y. Ran, Y. Bengio, “Quantized neural networks: Training neural networks with low precision weights and activations”, The Journal of Machine Learning Research, vol. 18, no. 1, pp. 6869-6898, 2017.
[15] B. Jacob, S. Kligys, B. Chen, M. Zhu, M. Tang, A. Howard, H. Adam, D. Kalenichenko, "Quantization and training of neural networks for efficient integer-arithmetic-only inference", Paper presented at the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018.
[16] R. Krishnamoorthi, “Quantizing deep convolutional networks for efficient inference: A whitepaper”, arXiv preprint arXiv:1806.08342, 2018.
[17] UrbanSound8K, (2018). Urban Sound Datasets. from https://urbansounddataset.weebly.com/urbansound8k.html
[18] X. Zhu, M. Kaznady, G. Hendry, (2018). Hearing AI: Getting Started with Deep Learning for Audio on Azure. from https://blogs.technet.microsoft.com/machinelearning/2018/01/30/hearing-ai-getting-started-with-deep-learning-for-audio-on-azure/ |