Publications

You can also find my articles on Google Scholar.

Publications

    Under Review

    Book Chapters

  1. Federated Learning for Resource-Constrained IoT Devices: Panoramas and State-of-the-art, Book Name: Federated and Transfer Learning, Publisher: Springer, To Appear, 2022
  2. Published Papers

  3. PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts
    ACL Demos Track 2022
    [Arxiv Link]
  4. Multitask Prompt Tuning Enables Zero-Shot Task Generalization ( ICLR 2022 )
    Paper - https://arxiv.org/abs/2110.08207
    Model - https://huggingface.co/bigscience/T0pp
    Github link to dataset - https://github.com/bigscience-workshop/promptsource/
    The Tenth International Conference on Learning Representations, April 2022
  5. MLPerf Tiny Benchmark
    Colby Banbury, Vijay Janapa Reddi, Peter Torelli, Jeremy Holleman, Nat Jeffries, Csaba Kiraly, Pietro Montino, David Kanter, Sebastian Ahmed, Danilo Pau, Urmish Thakker , Antonio Torrini, Peter Warden, Jay Cordaro, Giuseppe Di Guglielmo, Javier Duarte, Stephen Gibellini, Videet Parekh, Honson Tran, Nhan Tran, Niu Wenxu, Xu Xuesong ( NeurIPS 2021 )
    [arxiv link][Openreview Link]
    Thirty-fifth Conference on Neural Information Processing Systems, Dec 2021
  6. MicroNets: Neural Network Architectures for Deploying TinyML Applications on Commodity Microcontrollers ( MLSys 2021 )
    Colby Banbury, Chuteng Zhou, Igor Fedorov, Ramon Matas Navarro, Urmish Thakkar, Dibakar Gope, Vijay Janapa Reddi, Matthew Mattina, Paul N. Whatmough
    [Arxiv][MLSys Link]
    Fourth Conference on Machine Learning and Systems, April 2021
  7. Doping: A Technique for Extreme Compression of LSTM Models using Sparse Additive Matrices( MLSys 2021 )
    Urmish Thakker , Paul N. Whatmough, Zhi-Gang Liu, Matthew Mattina, Jesse Beu
    [ MLSys Link ][ Arxiv Link]
    Fourth Conference on Machine Learning and Systems, April 2021
  8. Compressing RNNs to Kilobyte Budget for IoT Devices Using Kronecker Products ( JETC 2021 )
    Urmish Thakker, Jesse Beu, Dibakar Gope, Chu Zhou, Igor Fedorov, Ganesh Dasika and Matthew Mattina
    To appear, ACM Journal on Emerging Technologies in Computing Systems, 2021
    [Arxiv Link][ACM Link]
  9. Federated Learning for Resource-Constrained IoT Devices: Panoramas and State-of-the-art
    Ahmed Imteaj, Urmish Thakker, Shiqiang Wang, Jian Li and M. Hadi Amini
    ( IOTJ 2021 ) [Arxiv] [ IEEE Link ]
    IEEE Internet of Things Journal, July 2021
  10. Rank and Run-time aware compression of NLP Applications ( SustaiNLP-EMNLP 2020 )
    Urmish Thakker, Jesse Beu, Dibakar Gope, Ganesh Dasika and Matthew Mattina
    First Workshop on Simple and Efficient Natural Language Processing at The Conference on Empirical Methods in Natural Language Processing (EMNLP), Nov 2020
    Links [Workshop][ Arxiv Paper ][ACL Link]
  11. Pushing the Envelope of Dynamic Spatial Gating technologies ( AIChallengeIoT 2020 )
    Xueqin Huang, Urmish Thakker , Dibakar Gope, Jesse Beu
    2nd International Workshop on Challenges in Artificial Intelligence and Machine Learning for Internet of Things at ACM SenSys, Nov 2020
    Links [Workshop][ACM Link ]
  12. Understanding the Impact of Dynamic Channel Pruning on Conditionally Parameterized Convolutions ( AIChallengeIoT 2020 )
    Ravi Raju, Dibakar Gope, Urmish Thakker , Jesse Beu
    2nd International Workshop on Challenges in Artificial Intelligence and Machine Learning for Internet of Things at ACM SenSys, Nov 2020
    Links [Workshop][ ACM Link ]
  13. Ternary MobileNets via Per-Layer Hybrid Filter Banks ( Joint Workshop on Efficient Deep Learning in Computer Vision )
    Dibakar Gope, Jesse Beu, Urmish Thakker , Matthew Mattina
    Joint Workshop on Efficient Deep Learning in Computer Vision at Conference on Computer Vision and Pattern Recognition (CVPR), June 2020
    Links [Workshop][Paper]
  14. Pushing the limits of RNN Compression (NeurIPS-EMC2 2019)
    Urmish Thakker, Igor Fedorov, Jesse Beu, Dibakar Gope, Chu Zhou, Ganesh Dasika and Matthew Mattina
    5th Workshop on Energy Efficient Machine Learning and Cognitive Computing, Co-located with the 33rd Conference on Neural Information Processing Systems (NeurIPS), Dec. 2019.
    Links [Workshop][Arxiv Paper][ IEEE Link]
  15. Skipping RNN State Updates without Retraining the Original Model* (SenSys-ML 2019)
    Jin Tao, Urmish Thakker, Ganesh Dasika, Jesse Beu
    1st Workshop on Machine Learning on Edge in Sensor Systems (Sensys-ML), Co-located with 17th ACM Conference on Embedded Networked Sensor Systems (SenSys 2019), Nov. 2019
    Links [Workshop][Paper]
    *Won the best paper award
  16. Run-Time Efficient RNN Compression for Inference on Edge Device (ISCA-EMC2 2019)
    Urmish Thakker, Jesse Beu, Dibakar Gope, Ganesh Dasika and Matthew Mattina
    4th Workshop on Energy Efficient Machine Learning and Cognitive Computing for Embedded Applications (EMC2), Co-located with the 46th Int. Symp on Computer Architecture (ISCA), Jun. 2019.
    Links [Workshop][Paper]

    Peer Reviewed Workshop Papers

  17. Doping: A Technique for Extreme Compression of LSTM Models using Sparse Additive Matrices ( SNN Workshop 2021 )
    Urmish Thakker, Paul Whatmough, Zhi-Gang Liu, Matthew Mattina, Jesse Beu
    Sparsity in Neural Networks: Advancing Understanding and Practice, July 2021
    Links [Workshop]
  18. Doped Structured Matrices for Extreme Compression of LSTM Models ( SustaiNLP-EMNLP 2020 )
    Urmish Thakker, Paul Whatmough, Zhi-Gang Liu, Matthew Mattina, Jesse Beu
    First Workshop on Simple and Efficient Natural Language Processing at The Conference on Empirical Methods in Natural Language Processing (EMNLP), Nov 2020
    Links [Workshop]
  19. Benchmarking TinyML Systems: Challenges and Direction* (Benchmarking Machine Learning Workloads on Emerging Hardware Workshop)
    Colby Banbury , Vijay Janapa Reddi , Will Fu , Max Lam , Amin Fazel , Jeremy Holleman , Xinyuan Huang , Robert Hurtado , David Kanter , Anton Lokhmotov , David Patterson , Danilo Pau , Jeff Sieracki , Jae-Sun Seo , Urmish Thakkar, Marian Verhelst , Poonam Yadav
    First International Workshop on Benchmarking Machine Learning Workloads on Emerging Hardware at Third Conference on Machine Learning and Systems (MLSys), March 2020
    *As part of the TinyML Performance Working Group
    Links [Workshop][Paper]
  20. Compressing Language Models using Doped Kronecker Products (On-device Intelligence Workshop)
    Urmish Thakker , Paul Whatmough, Matthew Mattina, Jesse Beu
    On-device Intelligence Workshop at Third Conference on Machine Learning and Systems (MLSys), March 2020
    Links [Workshop][Paper][Video]
  21. A Static Analysis-based Cross-Architecture Performance Prediction Using Machine Learning (ISCA-AIDArc 2019)
    Newsha Ardalani, Urmish Thakker, Aws Albarghouthi, Karu Sankaralingam
    2nd International Workshop on AI-assisted Design for Architecture co-located with 46th Int. Symposium on Computer Architecture (ISCA), Jun. 2019
    Links [Workshop][Paper]
  22. Measuring scheduling efficiency of RNNs for NLP applications (ISPASS-Fasthpath 2019)
    Urmish Thakker, Ganesh Dasika, Jesse Beu, Matthew Mattina
    6th edition of International Workshop on Performance Analysis of Machine Learning Systems (Fastpath) co-located with IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS), March 2019.
    Links [Workshop][Paper]

    Extended Abstracts/ Posters

  23. Hardware Aware Dynamic Inference Technologies (tinyML 2021)
    tinyML Summit 2021 [Link to Summit][Recorded Talk]
  24. Improving accuracy of neural networks compressed using fixed structures via doping (tinyML 2020)
    Urmish Thakker , Ganesh Dasika, Paul Whatmough, Matthew Mattina, Jesse Beu
    tinyML Summit 2020 [Link to Summit][Poster]
  25. Aggressive Compression of MobileNets Using Hybrid Ternary Layers (tinyML 2020)
    Dibakar Gope, Jesse Beu, Urmish Thakker , and Matthew Mattina
    tinyML Summit 2020 [Link to Summit][Poster]
  26. RNN Compression using Hybrid Matrix Decomposition (tinyML 2019)
    Urmish Thakker, Ganesh Dasika, Jesse Beu, Dibakar Gope, and Matthew Mattina
    tinyML Summit, Mar. 2019.
    Links [Link to Summit][Poster]