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ZeroQuant-HERO: Hardware-Enhanced Robust Optimized Post-Training Quantization Framework for W8A8 Transformers.

Z. Yao, R. Aminabadi, S. Youn, X. Wu, E. Zheng, and Y. He. CoRR, (2023)

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Enayatollah Yazdani

Ibrahim Yazdani

Shams Yazdani

Fahema Yazdani

Sima Yazdani

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SHARP: An Adaptable, Energy-Efficient Accelerator for Recurrent Neural Networks.R. Aminabadi, O. Ruwase, M. Zhang, Y. He, J. Arnau, and A. González. ACM Trans. Embed. Comput. Syst., 22 (2): 30:1-30:23 (March 2023)Understanding INT4 Quantization for Transformer Models: Latency Speedup, Composability, and Failure Cases.X. Wu, C. Li, R. Aminabadi, Z. Yao, and Y. He. CoRR, (2023)Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model.S. Smith, M. Patwary, B. Norick, P. LeGresley, S. Rajbhandari, J. Casper, Z. Liu, S. Prabhumoye, G. Zerveas, V. Korthikanti and 10 other author(s). CoRR, (2022)ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers.Z. Yao, R. Aminabadi, M. Zhang, X. Wu, C. Li, and Y. He. NeurIPS, (2022)ZeroQuant(4+2): Redefining LLMs Quantization with a New FP6-Centric Strategy for Diverse Generative Tasks.X. Wu, H. Xia, S. Youn, Z. Zheng, S. Chen, A. Bakhtiari, M. Wyatt, R. Aminabadi, Y. He, O. Ruwase and 2 other author(s). CoRR, (2023)Understanding Int4 Quantization for Language Models: Latency Speedup, Composability, and Failure Cases.X. Wu, C. Li, R. Aminabadi, Z. Yao, and Y. He. ICML, volume 202 of Proceedings of Machine Learning Research, page 37524-37539. PMLR, (2023)ZeRO-Offload: Democratizing Billion-Scale Model Training.J. Ren, S. Rajbhandari, R. Aminabadi, O. Ruwase, S. Yang, M. Zhang, D. Li, and Y. He. USENIX Annual Technical Conference, page 551-564. USENIX Association, (2021)DeepSpeed-FastGen: High-throughput Text Generation for LLMs via MII and DeepSpeed-Inference.C. Holmes, M. Tanaka, M. Wyatt, A. Awan, J. Rasley, S. Rajbhandari, R. Aminabadi, H. Qin, A. Bakhtiari, L. Kurilenko and 1 other author(s). CoRR, (2024)ZeroQuant-HERO: Hardware-Enhanced Robust Optimized Post-Training Quantization Framework for W8A8 Transformers.Z. Yao, R. Aminabadi, S. Youn, X. Wu, E. Zheng, and Y. He. CoRR, (2023)DeepSpeed- Inference: Enabling Efficient Inference of Transformer Models at Unprecedented Scale.R. Aminabadi, S. Rajbhandari, A. Awan, C. Li, D. Li, E. Zheng, O. Ruwase, S. Smith, M. Zhang, J. Rasley and 1 other author(s). SC, page 46:1-46:15. IEEE, (2022)

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Disambiguation of "Aminabadi, Reza Yazdani"

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ZeroQuant-HERO: Hardware-Enhanced Robust Optimized Post-Training Quantization Framework for W8A8 Transformers.

Please choose a person to relate this publication to

Enayatollah Yazdani

Ibrahim Yazdani

Shams Yazdani

Fahema Yazdani

Sima Yazdani

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Aminabadi, Reza Yazdani"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML ZeroQuant-HERO: Hardware-Enhanced Robust Optimized Post-Training Quantization Framework for W8A8 Transformers.

Please choose a person to relate this publication to

Enayatollah Yazdani

Ibrahim Yazdani

Shams Yazdani

Fahema Yazdani

Sima Yazdani

Other publications of authors with the same name

Disambiguation

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URL
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ZeroQuant-HERO: Hardware-Enhanced Robust Optimized Post-Training Quantization Framework for W8A8 Transformers.