FLO Physical Layer: An Overview. - Murali R. Chari; Fuyun Ling; Ashok Mantravadi; Raghuraman Krishnamoorthi; Rajiv Vijayan; G. Kent Walker; Rob Chandhok (IEEE Trans. Broadcast., 2007)

Quantizing deep convolutional networks for efficient inference: A whitepaper. - Raghuraman Krishnamoorthi (CoRR, 2018)

Deep Learning Recommendation Model for Personalization and Recommendation Systems. - Maxim Naumov; Dheevatsa Mudigere; Hao-Jun Michael Shi; Jianyu Huang; Narayanan Sundaraman; Jongsoo Park; Xiaodong Wang 020; Udit Gupta; Carole-Jean Wu; Alisson G. Azzolini; Dmytro Dzhulgakov; Andrey Mallevich; et al. (CoRR, 2019)

Check-N-Run: A Checkpointing System for Training Recommendation Models. - Assaf Eisenman; Kiran Kumar Matam; Steven Ingram; Dheevatsa Mudigere; Raghuraman Krishnamoorthi; Murali Annavaram; Krishnakumar Nair; Misha Smelyanskiy (CoRR, 2020)

Low-Precision Hardware Architectures Meet Recommendation Model Inference at Scale. - Zhaoxia Deng; Jongsoo Park; Ping Tak Peter Tang; Haixin Liu; Jie Yang; Hector Yuen; Jianyu Huang; Daya Shanker Khudia; Xiaohan Wei; Ellie Wen; Dhruv Choudhary; Raghuraman Krishnamoorthi; et al. (IEEE Micro, 2021)

DepthShrinker: A New Compression Paradigm Towards Boosting Real-Hardware Efficiency of Compact Neural Networks. - Yonggan Fu; Haichuan Yang; Jiayi Yuan 001; Meng Li 004; Cheng Wan 005; Raghuraman Krishnamoorthi; Vikas Chandra; Yingyan Lin (ICML, 2022)

BiT: Robustly Binarized Multi-distilled Transformer. - Zechun Liu; Barlas Oguz; Aasish Pappu; Lin Xiao; Scott Yih; Meng Li 004; Raghuraman Krishnamoorthi; Yashar Mehdad (NeurIPS, 2022)

Check-N-Run: a Checkpointing System for Training Deep Learning Recommendation Models. - Assaf Eisenman; Kiran Kumar Matam; Steven Ingram; Dheevatsa Mudigere; Raghuraman Krishnamoorthi; Krishnakumar Nair; Misha Smelyanskiy; Murali Annavaram (NSDI, 2022)

Learning a Dual-Mode Speech Recognition Model VIA Self-Pruning. - Chunxi Liu; Yuan Shangguan; Haichuan Yang; Yangyang Shi; Raghuraman Krishnamoorthi; Ozlem Kalinli (SLT, 2022)

LiCo-Net: Linearized Convolution Network for Hardware-efficient Keyword Spotting. - Haichuan Yang; Zhaojun Yang; Li Wan; Biqiao Zhang; Yangyang Shi; Yiteng Huang; Ivaylo Enchev; Limin Tang; Raziel Alvarez; Ming Sun 013; Xin Lei; Raghuraman Krishnamoorthi; et al. (CoRR, 2022)

Binary and Ternary Natural Language Generation. - Zechun Liu; Barlas Oguz; Aasish Pappu; Yangyang Shi; Raghuraman Krishnamoorthi (ACL (1), 2023)

Fast Point Cloud Generation with Straight Flows. - Lemeng Wu; Dilin Wang; Chengyue Gong; Xingchao Liu; Yunyang Xiong; Rakesh Ranjan; Raghuraman Krishnamoorthi; Vikas Chandra; Qiang Liu 001 (CVPR, 2023)

MiniGPT-v2: large language model as a unified interface for vision-language multi-task learning. - Jun Chen 021; Deyao Zhu; Xiaoqian Shen; Xiang Li; Zechun Liu; Pengchuan Zhang; Raghuraman Krishnamoorthi; Vikas Chandra; Yunyang Xiong; Mohamed Elhoseiny (CoRR, 2023)

Gen2Det: Generate to Detect. - Saksham Suri; Fanyi Xiao; Animesh Sinha; Sean Chang Culatana; Raghuraman Krishnamoorthi; Chenchen Zhu; Abhinav Shrivastava (CoRR, 2023)

SqueezeSAM: User friendly mobile interactive segmentation. - Balakrishnan Varadarajan; Bilge Soran; Forrest N. Iandola; Xiaoyu Xiang; Yunyang Xiong; Lemeng Wu; Chenchen Zhu; Raghuraman Krishnamoorthi; Vikas Chandra (CoRR, 2023)

PathFusion: Path-Consistent Lidar-Camera Deep Feature Fusion. - Lemeng Wu; Dilin Wang; Meng Li 004; Yunyang Xiong; Raghuraman Krishnamoorthi; Qiang Liu 001; Vikas Chandra (3DV, 2024)

Mixture-of-Supernets: Improving Weight-Sharing Supernet Training with Architecture-Routed Mixture-of-Experts. - Ganesh Jawahar; Haichuan Yang; Yunyang Xiong; Zechun Liu; Dilin Wang; Fei Sun; Meng Li 004; Aasish Pappu; Barlas Oguz; Muhammad Abdul-Mageed; Laks V. S. Lakshmanan; Raghuraman Krishnamoorthi; et al. (ACL (Findings), 2024)

LLM-QAT: Data-Free Quantization Aware Training for Large Language Models. - Zechun Liu; Barlas Oguz; Changsheng Zhao 002; Ernie Chang; Pierre Stock; Yashar Mehdad; Yangyang Shi; Raghuraman Krishnamoorthi; Vikas Chandra (ACL (Findings), 2024)

EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything. - Yunyang Xiong; Bala Varadarajan; Lemeng Wu; Xiaoyu Xiang; Fanyi Xiao; Chenchen Zhu; Xiaoliang Dai; Dilin Wang; Fei Sun; Forrest N. Iandola; Raghuraman Krishnamoorthi; Vikas Chandra (CVPR, 2024)

TODM: Train Once Deploy Many Efficient Supernet-Based RNN-T Compression For On-Device ASR Models. - Yuan Shangguan; Haichuan Yang; Danni Li; Chunyang Wu; Yassir Fathullah; Dilin Wang; Ayushi Dalmia; Raghuraman Krishnamoorthi; Ozlem Kalinli; Junteng Jia; Jay Mahadeokar; Xin Lei; et al. (ICASSP, 2024)

Sparse Cocktail: Every Sparse Pattern Every Sparse Ratio All At Once. - Zhangheng Li; Shiwei Liu 003; Tianlong Chen 001; Ajay Kumar Jaiswal; Zhenyu Zhang 015; Dilin Wang; Raghuraman Krishnamoorthi; Shiyu Chang; Zhangyang Wang (ICML, 2024)

MobileLLM: Optimizing Sub-billion Parameter Language Models for On-Device Use Cases. - Zechun Liu; Changsheng Zhao 002; Forrest N. Iandola; Chen Lai; Yuandong Tian; Igor Fedorov; Yunyang Xiong; Ernie Chang; Yangyang Shi; Raghuraman Krishnamoorthi; Liangzhen Lai; Vikas Chandra (ICML, 2024)

Communication Efficient Distributed Training with Distributed Lion. - Bo Liu 042; Lemeng Wu; Lizhang Chen; Kaizhao Liang; Jiaxu Zhu; Chen Liang; Raghuraman Krishnamoorthi; Qiang Liu 001 (NeurIPS, 2024)

Data Efficient Reflow for Few Step Audio Generation. - Lemeng Wu; Zhaoheng Ni; Bowen Shi; Gaël Le Lan; Anurag Kumar 003; Varun Nagaraja; Xinhao Mei; Yunyang Xiong; Bilge Soran; Raghuraman Krishnamoorthi; Wei-Ning Hsu; Yangyang Shi; et al. (SLT, 2024)

Agent-as-a-Judge: Evaluate Agents with Agents. - Mingchen Zhuge; Changsheng Zhao 002; Dylan R. Ashley; Wenyi Wang; Dmitrii Khizbullin; Yunyang Xiong; Zechun Liu; Ernie Chang; Raghuraman Krishnamoorthi; Yuandong Tian; Yangyang Shi; Vikas Chandra; et al. (CoRR, 2024)

LongVU: Spatiotemporal Adaptive Compression for Long Video-Language Understanding. - Xiaoqian Shen; Yunyang Xiong; Changsheng Zhao 002; Lemeng Wu; Jun Chen 021; Chenchen Zhu; Zechun Liu; Fanyi Xiao; Balakrishnan Varadarajan; Florian Bordes; Zhuang Liu; Hu Xu 001; et al. (CoRR, 2024)

Llama Guard 3-1B-INT4: Compact and Efficient Safeguard for Human-AI Conversations. - Igor Fedorov; Kate Plawiak; Lemeng Wu; Tarek Elgamal; Naveen Suda; Eric Smith; Hongyuan Zhan; Jianfeng Chi; Yuriy Hulovatyy; Kimish Patel; Zechun Liu; Changsheng Zhao 002; et al. (CoRR, 2024)

Efficient Track Anything. - Yunyang Xiong; Chong Zhou; Xiaoyu Xiang; Lemeng Wu; Chenchen Zhu; Zechun Liu; Saksham Suri; Balakrishnan Varadarajan; Ramya Akula; Forrest N. Iandola; Raghuraman Krishnamoorthi; Bilge Soran; et al. (CoRR, 2024)

EdgeTAM: On-Device Track Anything Model. - Chong Zhou; Chenchen Zhu; Yunyang Xiong; Saksham Suri; Fanyi Xiao; Lemeng Wu; Raghuraman Krishnamoorthi; Bo Dai 027; Chen Change Loy; Vikas Chandra; Bilge Soran (CVPR, 2025)

SpinQuant: LLM Quantization with Learned Rotations. - Zechun Liu; Changsheng Zhao 002; Igor Fedorov; Bilge Soran; Dhruv Choudhary; Raghuraman Krishnamoorthi; Vikas Chandra; Yuandong Tian; Tijmen Blankevoort (ICLR, 2025)

Diversify, Don't Fine-Tune: Scaling Up Visual Recognition Training with Synthetic Images. - Zhuoran Yu; Chenchen Zhu; Sean Culatana; Raghuraman Krishnamoorthi; Fanyi Xiao; Yong Jae Lee (Trans. Mach. Learn. Res., 2025)

EdgeTAM: On-Device Track Anything Model. - Chong Zhou; Chenchen Zhu; Yunyang Xiong; Saksham Suri; Fanyi Xiao; Lemeng Wu; Raghuraman Krishnamoorthi; Bo Dai 002; Chen Change Loy; Vikas Chandra; Bilge Soran (CoRR, 2025)

ParetoQ: Scaling Laws in Extremely Low-bit LLM Quantization. - Zechun Liu; Changsheng Zhao 002; Hanxian Huang; Sijia Chen; Jing Zhang; Jiawei Zhao; Scott Roy; Lisa Jin; Yunyang Xiong; Yangyang Shi; Lin Xiao; Yuandong Tian; et al. (CoRR, 2025)

Scalable and Performant Data Loading. - Moto Hira; Christian Puhrsch; Valentin Andrei; Roman Malinovskyy; Gaël Le Lan; Abhinandan Krishnan; Joseph Cummings; Miguel Martin; Gokul Gunasekaran; Yuta Inoue; Alex J. Turner; Raghuraman Krishnamoorthi (CoRR, 2025)