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Recurrent neural networks can be large and compute-intensive, yet many applications that benefit from RNNs run on small devices with very limited compute and storage capabilities while still having run-time constraints.
Measuring scheduling efficiency of RNNs for NLP applications
Urmish Thakker, Ganesh Dasika, Jesse G. Beu, and Matthew Mattina. 2019b · 1904
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Compressing RNNs for IoT devices by 15-38x using Kronecker Products
Urmish Thakker, Jesse G. Beu, Dibakar Gope, Chu Zhou, Igor Fedorov, Ganesh Dasika, and Matthew Mattina. 2019a · 1906
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Ternary MobileNets via Per-Layer Hybrid Filter Banks
Dibakar Gope, Jesse Beu, Urmish Thakker, and Matthew Mattina. 2019 · 1911
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Compressing Language Models using Doped Kronecker Products
Urmish Thakker, Paul Whatmough, Matthew Mattina, and Jesse Beu. 2020 · 2001
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Recurrent Neural Network Regularization
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Structured Transforms for Small-Footprint Deep Learning
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Deep, convolutional, and recurrent models for human activity recognition using wearables
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Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding
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Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition
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CirCNN: Accelerating and Compressing Deep Neural Networks Using Block-circulant Weight Matrices. In Proceedings of the 50th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO-50 ’17) . ACM, New York, NY, USA, 395–408
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To prune, or not to prune: exploring the efficacy of pruning for model compression
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Adaptive Mixture of Low-Rank Factorizations for Compact Neural Modeling
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Structured Weight Matrices-Based Hardware Accelerators in Deep Neural Networks: FPGAs and ASICs. In Proceedings of the 2018 on Great Lakes Symposium on VLSI (GLSVLSI ’18) . ACM, New York, NY, USA, 353–358
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Efficient Recurrent Neural Networks using Structured Matrices in FPGAs
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Cited alongside, same era.
Neural Networks Compression for Language Modeling. In Pattern Recognition and Machine Intelligence , B. Uma Shankar, Kuntal Ghosh, Deba Prasad Mandal, Shubhra Sankar Ray, David Zhang, and Sankar K. Pal (Eds.). Springer International Publishing, Cham, 351–357
Artem M. Grachev, Dmitry I. Ignatov, and Andrey V. Savchenko. 2017 · 2017
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Factorization tricks for LSTM networks
Oleksii Kuchaiev and Boris Ginsburg. 2017 · 2017
Cited alongside, same era.
Zhe Li, Shuo Wang, Caiwen Ding, Qinru Qiu, Yanzhi Wang, and Yun Liang. 2018 · 2018
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Learning Compressed Transforms with Low Displacement Rank
Anna Thomas, Albert Gu, Tri Dao, Atri Rudra, and Christopher Ré. 2018 · 2018
Later among the works it cites.
Pushing the limits of RNN Compression
Urmish Thakker, Igor Fedorov, Jesse G. Beu, Dibakar Gope, Chu Zhou, Ganesh Dasika, and Matthew Mattina. 2019c · 2019
Closest in time.