Papers by Raghuraman Krishnamoorthi
Mixture-of-Supernets: Improving Weight-Sharing Supernet Training with Architecture-Routed Mixture-of-Experts (2024.findings-acl)
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Ganesh Jawahar, Haichuan Yang, Yunyang Xiong, Zechun Liu, Dilin Wang, Fei Sun, Meng Li, Aasish Pappu, Barlas Oguz, Muhammad Abdul-Mageed, Laks Lakshmanan, Raghuraman Krishnamoorthi, Vikas Chandra
| Challenge: | Neural architecture search (NAS) uses weight-sharing supernets to generate diverse subnetworks without retraining. |
| Approach: | They propose a weight-sharing supernet that leverages mixture-of-experts to enhance supernet model expressiveness with minimal training overhead. |
| Outcome: | The proposed method achieves state-of-the-art (SoTA) performance in NAS for fast machine translation models, surpassing NAS-BERT and AutoDistil across various model sizes. |
Binary and Ternary Natural Language Generation (2023.acl-long)
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| Challenge: | ternary and binary neural networks have proven difficult to optimize since both parameter and output space are discretized . authors demonstrate ternaries and binary models on downstream tasks of summarization and machine translation . |
| Approach: | They propose to use ternary and binary neural networks to optimize for multiplication-free computation . they propose to apply statistics-based quantization for the weights and elastic quantization of the activations to the transformer text generation model. |
| Outcome: | The proposed model outperforms the best existing models on machine translation tasks. |
LLM-QAT: Data-Free Quantization Aware Training for Large Language Models (2024.findings-acl)
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Zechun Liu, Barlas Oguz, Changsheng Zhao, Ernie Chang, Pierre Stock, Yashar Mehdad, Yangyang Shi, Raghuraman Krishnamoorthi, Vikas Chandra
| Challenge: | Several post-training quantization methods have been shown to perform well down to 8-bits. |
| Approach: | They propose a data-free distillation method that leverages generations produced by the pre-trained model to quantize any generative model independent of its training data. |
| Outcome: | The proposed method outperforms SoTA PTQ and LLaMA models at low bit precision. |
MobileLLM-Flash: Latency-Guided On-Device LLM Design for Industry Scale Deployment (2026.acl-industry)
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Hanxian Huang, Igor Fedorov, Andrey Gromov, Bernard Beckerman, Naveen Suda, David Eriksson, Maximilian Balandat, Rylan Conway, Patrick Huber, Chinnadhurai Sankar, Ayushi Dalmia, Zechun Liu, Lemeng Wu, Tarek Elgamal, Adithya Sagar, Vikas Chandra, Raghuraman Krishnamoorthi
| Challenge: | MobileLLM-Flash is a family of foundation models for efficient on-device use with strong capabilities. |
| Approach: | They propose a method for designing on-device large language models under mobile latency constraints using hardware-in-the-loop architecture search. |
| Outcome: | The proposed model is amenable to industry-scale deployment and is compatible with mobile runtimes like Executorch. |