DeepSpeed-Chat: Easy, Fast and Affordable RLHF Training of ChatGPT-like Models at All Scales

1 code implementation • 2 Aug 2023 • Zhewei Yao, Reza Yazdani Aminabadi, Olatunji Ruwase, Samyam Rajbhandari, Xiaoxia Wu, Ammar Ahmad Awan, Jeff Rasley, Minjia Zhang, Conglong Li, Connor Holmes, Zhongzhu Zhou, Michael Wyatt, Molly Smith, Lev Kurilenko, Heyang Qin, Masahiro Tanaka, Shuai Che, Shuaiwen Leon Song, Yuxiong He

ChatGPT-like models have revolutionized various applications in artificial intelligence, from summarization and coding to translation, matching or even surpassing human performance.

Pushing the Limits of Narrow Precision Inferencing at Cloud Scale with Microsoft Floating Point

no code implementations • NeurIPS 2020 • Bita Darvish Rouhani, Daniel Lo, Ritchie Zhao, Ming Liu, Jeremy Fowers, Kalin Ovtcharov , Anna Vinogradsky, Sarah Massengill , Lita Yang, Ray Bittner, Alessandro Forin, Haishan Zhu, Taesik Na, Prerak Patel, Shuai Che, Lok Chand Koppaka , Xia Song, Subhojit Som, Kaustav Das, Saurabh T, Steve Reinhardt , Sitaram Lanka, Eric Chung, Doug Burger

In this paper, we explore the limits of Microsoft Floating Point (MSFP), a new class of datatypes developed for production cloud-scale inferencing on custom hardware.

Image Classification Question Answering

AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing

1 code implementation • 23 Aug 2019 • Tong Geng, Ang Li, Runbin Shi, Chunshu Wu, Tianqi Wang, Yanfei Li, Pouya Haghi, Antonino Tumeo, Shuai Che, Steve Reinhardt, Martin Herbordt

Deep learning systems have been successfully applied to Euclidean data such as images, video, and audio.

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Software-defined Design Space Exploration for an Efficient DNN Accelerator Architecture

no code implementations • 18 Mar 2019 • Ye Yu, Yingmin Li, Shuai Che, Niraj K. Jha, Weifeng Zhang

It models the accelerator design task as a multi-dimensional optimization problem.

object-detection Object Detection

Hardware-Guided Symbiotic Training for Compact, Accurate, yet Execution-Efficient LSTM

no code implementations • 30 Jan 2019 • Hongxu Yin, Guoyang Chen, Yingmin Li, Shuai Che, Weifeng Zhang, Niraj K. Jha

In this work, we propose a hardware-guided symbiotic training methodology for compact, accurate, yet execution-efficient inference models.

Language Modelling Neural Network Compression +2