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Doping: A technique for efficient compression of LSTM models using sparse structured additive matrices


Feb 14, 2021
Urmish Thakker, Paul N. Whatmough, Zhigang Liu, Matthew Mattina, Jesse Beu

* Accepted to be published at MLSys 2021 

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Rank and run-time aware compression of NLP Applications


Oct 06, 2020
Urmish Thakker, Jesse Beu, Dibakar Gope, Ganesh Dasika, Matthew Mattina

* Published at [email protected] 2020. arXiv admin note: text overlap with arXiv:1906.04886 

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High Throughput Matrix-Matrix Multiplication between Asymmetric Bit-Width Operands


Aug 03, 2020
Dibakar Gope, Jesse Beu, Matthew Mattina


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Compressing Language Models using Doped Kronecker Products


Jan 31, 2020
Urmish Thakker, Paul Whatamough, Matthew Mattina, Jesse Beu

* Presented at On-device Intelligence Workshop at Third Conference on Machine Learning and Systems (MLSys) 2020 
* Link to Workshop - https://mlsys.org/Conferences/2020/Schedule?showEvent=1297 

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Ternary MobileNets via Per-Layer Hybrid Filter Banks


Nov 04, 2019
Dibakar Gope, Jesse Beu, Urmish Thakker, Matthew Mattina


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Pushing the limits of RNN Compression


Oct 09, 2019
Urmish Thakker, Igor Fedorov, Jesse Beu, Dibakar Gope, Chu Zhou, Ganesh Dasika, Matthew Mattina

* 5th edition of Workshop on Energy Efficient Machine Learning and Cognitive Computing at NeurIPS 2019 
* 6 pages. arXiv admin note: substantial text overlap with arXiv:1906.02876 

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Compressing RNNs for IoT devices by 15-38x using Kronecker Products


Jun 18, 2019
Urmish Thakker, Jesse Beu, Dibakar Gope, Chu Zhou, Igor Fedorov, Ganesh Dasika, Matthew Mattina


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Run-Time Efficient RNN Compression for Inference on Edge Devices


Jun 18, 2019
Urmish Thakker, Jesse Beu, Dibakar Gope, Ganesh Dasika, Matthew Mattina

* Published at 4th edition of Workshop on Energy Efficient Machine Learning and Cognitive Computing for Embedded Applications at International Symposium of Computer Architecture 2019, Phoenix, Arizona (https://www.emc2-workshop.com/isca-19) colocated with ISCA 2019 

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