Urmish Thakker

According to our database, Urmish Thakker authored at least 32 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Composition of Experts on the SN40L Reconfigurable Dataflow Unit.

Raghu Prabhakar, Ram Sivaramakrishnan, Darshan Gandhi, Yun Du, Mingran Wang, Xiangyu Song, Kejie Zhang, Tianren Gao, Angela Wang, Xiaoyan Li, Joshua Brot, Calvin Leung, Tuowen Zhao, Mark Gottscho, Zhengyu Chen, Kaizhao Liang, Swayambhoo Jain, Urmish Thakker, Kevin J. Brown, Kunle Olukotun

IEEE Micro, 2024

SubgoalXL: Subgoal-based Expert Learning for Theorem Proving.

Xueliang Zhao, Lin Zheng, Haige Bo, Changran Hu, Urmish Thakker, Lingpeng Kong

CoRR, 2024

Constructing Domain-Specific Evaluation Sets for LLM-as-a-judge.

Ravi Raju, Swayambhoo Jain, Bo Li, Jonathan Li, Urmish Thakker

CoRR, 2024

SambaNova SN40L: Scaling the AI Memory Wall with Dataflow and Composition of Experts.

Raghu Prabhakar, Ram Sivaramakrishnan, Darshan Gandhi, Yun Du, Mingran Wang, Xiangyu Song, Kejie Zhang, Tianren Gao, Angela Wang, Karen Li, Yongning Sheng, Joshua Brot, Denis Sokolov, Apurv Vivek, Calvin Leung, Arjun Sabnis, Jiayu Bai, Tuowen Zhao, Mark Gottscho, David Jackson, Mark Luttrell, Manish K. Shah, Edison Chen, Kaizhao Liang, Swayambhoo Jain, Urmish Thakker, Dawei Huang, Sumti Jairath, Kevin J. Brown, Kunle Olukotun

CoRR, 2024

SambaLingo: Teaching Large Language Models New Languages.

Zoltan Csaki, Bo Li, Jonathan Li, Qiantong Xu, Pian Pawakapan, Leon Zhang, Yun Du, Hengyu Zhao, Changran Hu, Urmish Thakker

CoRR, 2024

SambaNova SN40L: Scaling the AI Memory Wall with Dataflow and Composition of Experts.

Raghu Prabhakar, Ram Sivaramakrishnan, Darshan Gandhi, Yun Du, Mingran Wang, Xiangyu Song, Kejie Zhang, Tianren Gao, Angela Wang, Xiaoyan Li, Yongning Sheng, Joshua Brot, Denis Sokolov, Apurv Vivek, Calvin Leung, Arjun Sabnis, Jiayu Bai, Tuowen Zhao, Mark Gottscho, David Jackson, Mark Luttrell, Manish K. Shah, Zhengyu Chen, Kaizhao Liang, Swayambhoo Jain, Urmish Thakker, Dawei Huang, Sumti Jairath, Kevin J. Brown, Kunle Olukotun

Proceedings of the 57th IEEE/ACM International Symposium on Microarchitecture, 2024

2023

Efficiently Adapting Pretrained Language Models To New Languages.

Zoltan Csaki, Pian Pawakapan, Urmish Thakker, Qiantong Xu

CoRR, 2023

Training Large Language Models Efficiently with Sparsity and Dataflow.

Venkat Srinivasan, Darshan Gandhi, Urmish Thakker, Raghu Prabhakar

CoRR, 2023

2022

A Survey on Federated Learning for Resource-Constrained IoT Devices.

Ahmed Imteaj, Urmish Thakker, Shiqiang Wang, Jian Li, M. Hadi Amini

IEEE Internet Things J., 2022

PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts.

Stephen H. Bach, Victor Sanh, Zheng Xin Yong, Albert Webson, Colin Raffel, Nihal V. Nayak, Abheesht Sharma, Taewoon Kim, M. Saiful Bari, Thibault Févry, Zaid Alyafeai, Manan Dey, Andrea Santilli, Zhiqing Sun, Srulik Ben-David, Canwen Xu, Gunjan Chhablani, Han Wang, Jason Alan Fries, Maged Saeed AlShaibani, Shanya Sharma, Urmish Thakker, Khalid Almubarak, Xiangru Tang, Mike Tian-Jian Jiang, Alexander M. Rush

CoRR, 2022

Multitask Prompted Training Enables Zero-Shot Task Generalization.

Victor Sanh, Albert Webson, Colin Raffel, Stephen H. Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Arun Raja, Manan Dey, M Saiful Bari, Canwen Xu, Urmish Thakker, Shanya Sharma Sharma, Eliza Szczechla, Taewoon Kim, Gunjan Chhablani, Nihal V. Nayak, Debajyoti Datta, Jonathan Chang, Mike Tian-Jian Jiang, Han Wang, Matteo Manica, Sheng Shen, Zheng Xin Yong, Harshit Pandey, Rachel Bawden, Thomas Wang, Trishala Neeraj, Jos Rozen, Abheesht Sharma, Andrea Santilli, Thibault Févry, Jason Alan Fries, Ryan Teehan, Teven Le Scao, Stella Biderman, Leo Gao, Thomas Wolf, Alexander M. Rush

Proceedings of the Tenth International Conference on Learning Representations, 2022

PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts.

Stephen H. Bach, Victor Sanh, Zheng Xin Yong, Albert Webson, Colin Raffel, Nihal V. Nayak, Abheesht Sharma, Taewoon Kim, M. Saiful Bari, Thibault Févry, Zaid Alyafeai, Manan Dey, Andrea Santilli, Zhiqing Sun, Srulik Ben-David, Canwen Xu, Gunjan Chhablani, Han Wang, Jason Alan Fries, Maged Saeed AlShaibani, Shanya Sharma, Urmish Thakker, Khalid Almubarak, Xiangru Tang, Dragomir R. Radev, Mike Tian-Jian Jiang, Alexander M. Rush

Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, 2022

2021

Compressing RNNs to Kilobyte Budget for IoT Devices Using Kronecker Products.

Urmish Thakker, Igor Fedorov, Chu Zhou, Dibakar Gope, Matthew Mattina, Ganesh Dasika, Jesse G. Beu

ACM J. Emerg. Technol. Comput. Syst., 2021

Multitask Prompted Training Enables Zero-Shot Task Generalization.

Victor Sanh, Albert Webson, Colin Raffel, Stephen H. Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Teven Le Scao, Arun Raja, Manan Dey, M. Saiful Bari, Canwen Xu, Urmish Thakker, Shanya Sharma, Eliza Szczechla, Taewoon Kim, Gunjan Chhablani, Nihal V. Nayak, Debajyoti Datta, Jonathan Chang, Mike Tian-Jian Jiang, Han Wang, Matteo Manica, Sheng Shen, Zheng Xin Yong, Harshit Pandey, Rachel Bawden, Thomas Wang, Trishala Neeraj, Jos Rozen, Abheesht Sharma, Andrea Santilli, Thibault Févry, Jason Alan Fries, Ryan Teehan, Stella Biderman, Leo Gao, Tali Bers, Thomas Wolf, Alexander M. Rush

CoRR, 2021

MLPerf Tiny Benchmark.

Colby R. Banbury, Vijay Janapa Reddi, Peter Torelli, Jeremy Holleman, Nat Jeffries, Csaba Király, Pietro Montino, David Kanter, Sebastian Ahmed, Danilo Pau, Urmish Thakker, Antonio Torrini, Pete Warden, Jay Cordaro, Giuseppe Di Guglielmo, Javier M. Duarte, Stephen Gibellini, Videet Parekh, Honson Tran, Nhan Tran, Wenxu Niu, Xuesong Xu

CoRR, 2021

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

Urmish Thakker, Paul N. Whatmough, Zhi Gang Liu, Matthew Mattina, Jesse G. Beu

CoRR, 2021

MLPerf Tiny Benchmark.

Colby R. Banbury, Vijay Janapa Reddi, Peter Torelli, Nat Jeffries, Csaba Király, Jeremy Holleman, Pietro Montino, David Kanter, Pete Warden, Danilo Pau, Urmish Thakker, Antonio Torrini, Jay Cordaro, Giuseppe Di Guglielmo, Javier M. Duarte, Honson Tran, Nhan Tran, Wenxu Niu, Xuesong Xu

Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

Doping: A technique for Extreme Compression of LSTM Models using Sparse Structured Additive Matrices.

Urmish Thakker, Paul N. Whatmough, Zhi Gang Liu, Matthew Mattina, Jesse G. Beu

Proceedings of the Fourth Conference on Machine Learning and Systems, 2021

MicroNets: Neural Network Architectures for Deploying TinyML Applications on Commodity Microcontrollers.

Colby R. Banbury, Chuteng Zhou, Igor Fedorov, Ramon Matas Navarro, Urmish Thakker, Dibakar Gope, Vijay Janapa Reddi, Matthew Mattina, Paul N. Whatmough

Proceedings of the Fourth Conference on Machine Learning and Systems, 2021

2020

Benchmarking TinyML Systems: Challenges and Direction.

Colby R. Banbury, Vijay Janapa Reddi, Max Lam, William Fu, Amin Fazel, Jeremy Holleman, Xinyuan Huang, Robert Hurtado, David Kanter, Anton Lokhmotov, David A. Patterson, Danilo Pau, Jae-sun Seo, Jeff Sieracki, Urmish Thakker, Marian Verhelst, Poonam Yadav

CoRR, 2020

Federated Learning for Resource-Constrained IoT Devices: Panoramas and State-of-the-art.

Ahmed Imteaj, Urmish Thakker, Shiqiang Wang, Jian Li, M. Hadi Amini

CoRR, 2020

Compressing Language Models using Doped Kronecker Products.

Urmish Thakker, Paul N. Whatmough, Matthew Mattina, Jesse G. Beu

CoRR, 2020

Understanding the Impact of Dynamic Channel Pruning on Conditionally Parameterized Convolutions.

Ravi Raju, Dibakar Gope, Urmish Thakker, Jesse G. Beu

Proceedings of the AIChallengeIoT@SenSys 2020: Proceedings of the 2nd International Workshop on Challenges in Artificial Intelligence and Machine Learning for Internet of Things, 2020

Pushing the Envelope of Dynamic Spatial Gating technologies.

Xueqin Huang, Urmish Thakker, Dibakar Gope, Jesse G. Beu

Proceedings of the AIChallengeIoT@SenSys 2020: Proceedings of the 2nd International Workshop on Challenges in Artificial Intelligence and Machine Learning for Internet of Things, 2020

Rank and run-time aware compression of NLP Applications.

Urmish Thakker, Jesse G. Beu, Dibakar Gope, Ganesh Dasika, Matthew Mattina

Proceedings of SustaiNLP: Workshop on Simple and Efficient Natural Language Processing, 2020

Ternary MobileNets via Per-Layer Hybrid Filter Banks.

Dibakar Gope, Jesse G. Beu, Urmish Thakker, Matthew Mattina

Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

2019

A Static Analysis-based Cross-Architecture Performance Prediction Using Machine Learning.

Newsha Ardalani, Urmish Thakker, Aws Albarghouthi, Karu Sankaralingam

CoRR, 2019

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

Urmish Thakker, Jesse G. Beu, Dibakar Gope, Chu Zhou, Igor Fedorov, Ganesh Dasika, Matthew Mattina

CoRR, 2019

Measuring scheduling efficiency of RNNs for NLP applications.

Urmish Thakker, Ganesh Dasika, Jesse G. Beu, Matthew Mattina

CoRR, 2019

Skipping RNN State Updates without Retraining the Original Model.

Jin Tao, Urmish Thakker, Ganesh Dasika, Jesse G. Beu

Proceedings of the 1st Workshop on Machine Learning on Edge in Sensor Systems, 2019

Pushing the limits of RNN Compression.

Urmish Thakker, Igor Fedorov, Jesse G. Beu, Dibakar Gope, Chu Zhou, Ganesh Dasika, Matthew Mattina

Proceedings of the Fifth Workshop on Energy Efficient Machine Learning and Cognitive Computing, 2019

Run-Time Efficient RNN Compression for Inference on Edge Devices.

Urmish Thakker, Jesse G. Beu, Dibakar Gope, Ganesh Dasika, Matthew Mattina

Proceedings of the 2nd Workshop on Energy Efficient Machine Learning and Cognitive Computing for Embedded Applications, 2019