Raghuraman Krishnamoorthi

According to our database, Raghuraman Krishnamoorthi authored at least 25 papers between 2007 and 2024.

Collaborative distances:

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

2024

SpinQuant: LLM quantization with learned rotations.

Zechun Liu, Changsheng Zhao, Igor Fedorov, Bilge Soran, Dhruv Choudhary, Raghuraman Krishnamoorthi, Vikas Chandra, Yuandong Tian, Tijmen Blankevoort

CoRR, 2024

Communication Efficient Distributed Training with Distributed Lion.

Bo Liu, Lemeng Wu, Lizhang Chen, Kaizhao Liang, Jiaxu Zhu, Chen Liang, Raghuraman Krishnamoorthi, Qiang Liu

CoRR, 2024

MobileLLM: Optimizing Sub-billion Parameter Language Models for On-Device Use Cases.

Zechun Liu, Changsheng Zhao, Forrest N. Iandola, Chen Lai, Yuandong Tian, Igor Fedorov, Yunyang Xiong, Ernie Chang, Yangyang Shi, Raghuraman Krishnamoorthi, Liangzhen Lai, Vikas Chandra

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Sparse Cocktail: Every Sparse Pattern Every Sparse Ratio All At Once.

Zhangheng Li, Shiwei Liu, Tianlong Chen, Ajay Kumar Jaiswal, Zhenyu Zhang, Dilin Wang, Raghuraman Krishnamoorthi, Shiyu Chang, Zhangyang Wang

Proceedings of the Forty-first International Conference on Machine Learning, 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, Mike Seltzer, Vikas Chandra

Proceedings of the IEEE International Conference on Acoustics, 2024

LLM-QAT: Data-Free Quantization Aware Training for Large Language Models.

Zechun Liu, Barlas Oguz, Changsheng Zhao, Ernie Chang, Pierre Stock, Yashar Mehdad, Yangyang Shi, Raghuraman Krishnamoorthi, Vikas Chandra

Proceedings of the Findings of the Association for Computational Linguistics, 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, Aasish Pappu, Barlas Oguz, Muhammad Abdul-Mageed, Laks V. S. Lakshmanan, Raghuraman Krishnamoorthi, Vikas Chandra

Proceedings of the Findings of the Association for Computational Linguistics, 2024

PathFusion: Path-Consistent Lidar-Camera Deep Feature Fusion.

Lemeng Wu, Dilin Wang, Meng Li, Yunyang Xiong, Raghuraman Krishnamoorthi, Qiang Liu, Vikas Chandra

Proceedings of the International Conference on 3D Vision, 2024

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

Gen2Det: Generate to Detect.

Saksham Suri, Fanyi Xiao, Animesh Sinha, Sean Chang Culatana, Raghuraman Krishnamoorthi, Chenchen Zhu, Abhinav Shrivastava

CoRR, 2023

Diversify, Don't Fine-Tune: Scaling Up Visual Recognition Training with Synthetic Images.

Zhuoran Yu, Chenchen Zhu, Sean Chang Culatana, Raghuraman Krishnamoorthi, Fanyi Xiao, Yong Jae Lee

CoRR, 2023

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

CoRR, 2023

MiniGPT-v2: large language model as a unified interface for vision-language multi-task learning.

Jun Chen, Deyao Zhu, Xiaoqian Shen, Xiang Li, Zechun Liu, Pengchuan Zhang, Raghuraman Krishnamoorthi, Vikas Chandra, Yunyang Xiong, Mohamed Elhoseiny

CoRR, 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

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

Binary and Ternary Natural Language Generation.

Zechun Liu, Barlas Oguz, Aasish Pappu, Yangyang Shi, Raghuraman Krishnamoorthi

Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2023

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, Xin Lei, Raghuraman Krishnamoorthi, Vikas Chandra

CoRR, 2022

Learning a Dual-Mode Speech Recognition Model VIA Self-Pruning.

Chunxi Liu, Yuan Shangguan, Haichuan Yang, Yangyang Shi, Raghuraman Krishnamoorthi, Ozlem Kalinli

Proceedings of the IEEE Spoken Language Technology Workshop, 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

Proceedings of the 19th USENIX Symposium on Networked Systems Design and Implementation, 2022

BiT: Robustly Binarized Multi-distilled Transformer.

Zechun Liu, Barlas Oguz, Aasish Pappu, Lin Xiao, Scott Yih, Meng Li, Raghuraman Krishnamoorthi, Yashar Mehdad

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

DepthShrinker: A New Compression Paradigm Towards Boosting Real-Hardware Efficiency of Compact Neural Networks.

Yonggan Fu, Haichuan Yang, Jiayi Yuan, Meng Li, Cheng Wan, Raghuraman Krishnamoorthi, Vikas Chandra, Yingyan Lin

Proceedings of the International Conference on Machine Learning, 2022

2021

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, Carole-Jean Wu, Nadathur Satish, Changkyu Kim, Maxim Naumov, Sam Naghshineh, Mikhail Smelyanskiy

IEEE Micro, 2021

2020

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

2019

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, Udit Gupta, Carole-Jean Wu, Alisson G. Azzolini, Dmytro Dzhulgakov, Andrey Mallevich, Ilia Cherniavskii, Yinghai Lu, Raghuraman Krishnamoorthi, Ansha Yu, Volodymyr Kondratenko, Stephanie Pereira, Xianjie Chen, Wenlin Chen, Vijay Rao, Bill Jia, Liang Xiong, Misha Smelyanskiy

CoRR, 2019

2018

Quantizing deep convolutional networks for efficient inference: A whitepaper.

Raghuraman Krishnamoorthi

CoRR, 2018

2007

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