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Machine learning-based applications are increasingly prevalent in IoT devices.
Efficient keyword spotting using time delay neural networks
Myer, S. and Tomar, V. S · 1979
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Small-footprint keyword spotting using deep neural networks
Chen, G., Parada, C., and Heigold, G · 2014
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Speeding up convolutional neural networks with low rank expansions
Jaderberg, M., Vedaldi, A., and Zisserman, A · 2014
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Binaryconnect: Training deep neural networks with binary weights during propagations
Courbariaux, M., Bengio, Y., and David, J · 2015
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Han, S., Mao, H., and Dally, W. J · 2015
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Convolutional neural networks for small-footprint keyword spotting
Sainath, T. N. and Parada, C · 2015
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Convolutional neural networks with low-rank regularization
Tai, C., Xiao, T., Wang, X., and E, W · 2015
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Tensorflow: Large-scale machine learning on heterogeneous distributed systems
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I. J., Harp, A., Irving, G., Isard, M., Jia, Y., Józefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D. G., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P. A., Vanhoucke, V., Vasudevan, V., Viégas, F. B., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., and Zheng, X · 2016
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Ternary neural networks for resource-efficient AI applications
Alemdar, H., Caldwell, N., Leroy, V., Prost-Boucle, A., and Pétrot, F · 2016
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Dynamic network surgery for efficient dnns
Guo, Y., Yao, A., and Chen, Y · 2016
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Li, F. and Liu, B · 2016
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Going deeper with embedded fpga platform for convolutional neural network
Qiu, J., Wang, J., Yao, S., Guo, K., Li, B., Zhou, E., Yu, J., Tang, T., Xu, N., Song, S., Wang, Y., and Yang, H · 2016
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Xnor-net: Imagenet classification using binary convolutional neural networks
Rastegari, M., Ordonez, V., Redmon, J., and Farhadi, A · 2016
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Max-pooling loss training of long short-term memory networks for small-footprint keyword spotting
Sun, M., Raju, A., Tucker, G., Panchapagesan, S., Fu, G., Mandal, A., Matsoukas, S., Strom, N., and Vitaladevuni, S · 2016
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Learning structured sparsity in deep neural networks
Wen, W., Wu, C., Wang, Y., Chen, Y., and Li, H · 2016
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Zhu, C., Han, S., Mao, H., and Dally, W. J · 2016
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Net-trim: Convex pruning of deep neural networks with performance guarantee
Aghasi, A., Abdi, A., Nguyen, N., and Romberg, J · 2017
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Thinet: A filter level pruning method for deep neural network compression
Luo, J., Wu, J., and Lin, W · 2017
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Exploring sparsity in recurrent neural networks
Narang, S., Diamos, G. F., Sengupta, S., and Elsen, E · 2017
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Coordinating filters for faster deep neural networks
Wen, W., Xu, C., Wu, C., Wang, Y., Chen, Y., and Li, H · 2017
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Hello edge: Keyword spotting on microcontrollers
Zhang, Y., Suda, N., Lai, L., and Chandra, V · 2017
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To prune, or not to prune: exploring the efficacy of pruning for model compression
Zhu, M. and Gupta, S · 2017
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Convolutional recurrent neural networks for small-footprint keyword spotting
Arik, S. Ö., Kliegl, M., Child, R., Hestness, J., Gibiansky, A., Fougner, C., Prenger, R., and Coates, A · 2017
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Deep learning with low precision by half-wave gaussian quantization
Cai, Z., He, X., Sun, J., and Vasconcelos, N · 2017
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Channel pruning for accelerating very deep neural networks
He, Y., Zhang, X., and Sun, J · 2017
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Mobilenets: Efficient convolutional neural networks for mobile vision applications
Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H · 2017
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Resource-efficient machine learning in 2 KB RAM for the internet of things
Kumar, A., Goyal, S., and Varma, M · 2017
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Acoustic modeling for google home
Li, B., Sainath, T. N., Narayanan, A., Caroselli, J., Bacchiani, M., Misra, A., Shafran, I., Sak, H., Pundak, G., Chin, K. K., Sim, K. C., Weiss, R. J., Wilson, K. W., Variani, E., Kim, C., Siohan, O., Weintraub, M., McDermott, E., Rose, R., and Shannon, M · 2017
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Towards accurate binary convolutional neural network
Lin, X., Zhao, C., and Pan, W · 2017
Cited alongside, same era.
Efficient keyword spotting using dilated convolutions and gating
Coucke, A., Chlieh, M., Gisselbrecht, T., Leroy, D., Poumeyrol, M., and Lavril, T · 2018
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Binarycmd: Keyword spotting with deterministic binary basis
Fernández-Marqués, J., W.-S. Tseng, V., Bhattacharya, S., and D. Lane, N · 2018
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Morphnet: Fast & simple resource-constrained structure learning of deep networks
Gordon, A., Eban, E., Nachum, O., Chen, B., Wu, H., Yang, T., and Choi, E · 2018
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Edgespeechnets: Highly efficient deep neural networks for speech recognition on the edge
Lin, Z. Q., Chung, A. G., and Wong, A · 2018
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StrassenNets: Deep learning with a multiplication budget
Tschannen, M., Khanna, A., and Anandkumar, A · 2018
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Speech commands: A dataset for limited-vocabulary speech recognition
Warden, P · 2018
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Netadapt: Platform-aware neural network adaptation for mobile applications
Yang, T., Howard, A. G., Chen, B., Zhang, X., Go, A., Sandler, M., Sze, V., and Adam, H · 2018
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