Run-Time Efficient RNN Compression for Inference on Edge Devices. - Urmish Thakker; Jesse G. Beu; Dibakar Gope; Ganesh Dasika; Matthew Mattina (EMC2@HPCA/CVPR/ISCA, 2019)

Pushing the limits of RNN Compression. - Urmish Thakker; Igor Fedorov; Jesse G. Beu; Dibakar Gope; Chu Zhou; Ganesh Dasika; Matthew Mattina (EMC2@NeurIPS, 2019)

Skipping RNN State Updates without Retraining the Original Model. - Jin Tao; Urmish Thakker; Ganesh Dasika; Jesse G. Beu (SenSys-ML, 2019)

Measuring scheduling efficiency of RNNs for NLP applications. - Urmish Thakker; Ganesh Dasika; Jesse G. Beu; Matthew Mattina (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)

A Static Analysis-based Cross-Architecture Performance Prediction Using Machine Learning. - Newsha Ardalani; Urmish Thakker; Aws Albarghouthi; Karu Sankaralingam (CoRR, 2019)

Ternary MobileNets via Per-Layer Hybrid Filter Banks. - Dibakar Gope; Jesse G. Beu; Urmish Thakker; Matthew Mattina (CVPR Workshops, 2020)

Rank and run-time aware compression of NLP Applications. - Urmish Thakker; Jesse G. Beu; Dibakar Gope; Ganesh Dasika; Matthew Mattina (SustaiNLP@EMNLP, 2020)

Pushing the Envelope of Dynamic Spatial Gating technologies. - Xueqin Huang; Urmish Thakker; Dibakar Gope; Jesse G. Beu (AIChallengeIoT@SenSys, 2020)

Understanding the Impact of Dynamic Channel Pruning on Conditionally Parameterized Convolutions. - Ravi Raju; Dibakar Gope; Urmish Thakker; Jesse G. Beu (AIChallengeIoT@SenSys, 2020)

Compressing Language Models using Doped Kronecker Products. - Urmish Thakker; Paul N. Whatmough; Matthew Mattina; Jesse G. Beu (CoRR, 2020)

Federated Learning for Resource-Constrained IoT Devices: Panoramas and State-of-the-art. - Ahmed Imteaj; Urmish Thakker; Shiqiang Wang 001; Jian Li 008; M. Hadi Amini (CoRR, 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 001; Danilo Pau; et al. (CoRR, 2020)

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 (MLSys, 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 (MLSys, 2021)

MLPerf Tiny Benchmark. - Colby R. Banbury; Vijay Janapa Reddi; Peter Torelli; Nat Jeffries; Csaba Király 002; Jeremy Holleman; Pietro Montino; David Kanter; Pete Warden; Danilo Pau; Urmish Thakker; Antonio Torrini; et al. (NeurIPS Datasets and Benchmarks, 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; Jeremy Holleman; Nat Jeffries; Csaba Király 002; Pietro Montino; David Kanter; Sebastian Ahmed; Danilo Pau; Urmish Thakker; Antonio Torrini; et al. (CoRR, 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; et al. (CoRR, 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)

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 002; M. Saiful Bari; Thibault Févry; Zaid Alyafeai; Manan Dey; et al. (ACL (demo), 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; et al. (ICLR, 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 002; M. Saiful Bari; Thibault Févry; Zaid Alyafeai; Manan Dey; et al. (CoRR, 2022)

A Survey on Federated Learning for Resource-Constrained IoT Devices. - Ahmed Imteaj; Urmish Thakker; Shiqiang Wang 001; Jian Li 008; M. Hadi Amini (IEEE Internet Things J., 2022)

Training Large Language Models Efficiently with Sparsity and Dataflow. - Venkat Srinivasan; Darshan Gandhi; Urmish Thakker; Raghu Prabhakar (CoRR, 2023)

Efficiently Adapting Pretrained Language Models To New Languages. - Zoltan Csaki; Pian Pawakapan; Urmish Thakker; Qiantong Xu (CoRR, 2023)

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; et al. (MICRO, 2024)

SambaLingo: Teaching Large Language Models New Languages. - Zoltan Csaki; Bo Li; Jonathan Li 009; 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; Karen Li; Yongning Sheng; Joshua Brot; et al. (CoRR, 2024)

Constructing Domain-Specific Evaluation Sets for LLM-as-a-judge. - Ravi Raju; Swayambhoo Jain; Bo Li; Jonathan Li 009; Urmish Thakker (CoRR, 2024)

SubgoalXL: Subgoal-based Expert Learning for Theorem Proving. - Xueliang Zhao; Lin Zheng; Haige Bo; Changran Hu; Urmish Thakker; Lingpeng Kong (CoRR, 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; et al. (IEEE Micro, 2024)

Training Domain Draft Models for Speculative Decoding: Best Practices and Insights. - Fenglu Hong; Ravi Raju; Jonathan Lingjie Li; Bo Li; Urmish Thakker; Avinash Ravichandran; Swayambhoo Jain; Changran Hu (CoRR, 2025)

LLMs Know What to Drop: Self-Attention Guided KV Cache Eviction for Efficient Long-Context Inference. - Guangtao Wang; Shubhangi Upasani; Chen Wu; Darshan Gandhi; Jonathan Li 009; Changran Hu; Bo Li; Urmish Thakker (CoRR, 2025)

Distributed LLMs and Multimodal Large Language Models: A Survey on Advances, Challenges, and Future Directions. - M. Hadi Amini; Md. Jueal Mia; Yasaman Saadati; Ahmed Imteaj; Seyedsina Nabavirazavi; Urmish Thakker; Md. Zarif Hossain; Awal Ahmed Fime; S. S. Iyengar 001 (CoRR, 2025)

Synthetic Document Question Answering in Hungarian. - Jonathan Li 009; Zoltan Csaki; Nidhi Hiremath; Etash Kumar Guha; Fenglu Hong; Edward Ma; Urmish Thakker (CoRR, 2025)