1 code implementation • 12 Feb 2018 • Tianqi Chen, Thierry Moreau, Ziheng Jiang, Lianmin Zheng, Eddie Yan, Meghan Cowan, Haichen Shen, Leyuan Wang, Yuwei Hu, Luis Ceze, Carlos Guestrin, Arvind Krishnamurthy
Experimental results show that TVM delivers performance across hardware back-ends that are competitive with state-of-the-art, hand-tuned libraries for low-power CPU, mobile GPU, and server-class GPUs.
no code implementations • NeurIPS 2018 • Tianqi Chen, Lianmin Zheng, Eddie Yan, Ziheng Jiang, Thierry Moreau, Luis Ceze, Carlos Guestrin, Arvind Krishnamurthy
Efficient implementations of tensor operators, such as matrix multiplication and high dimensional convolution, are key enablers of effective deep learning systems.
no code implementations • 11 Jul 2018 • Thierry Moreau, Tianqi Chen, Luis Vega, Jared Roesch, Eddie Yan, Lianmin Zheng, Josh Fromm, Ziheng Jiang, Luis Ceze, Carlos Guestrin, Arvind Krishnamurthy
Specialized Deep Learning (DL) acceleration stacks, designed for a specific set of frameworks, model architectures, operators, and data types, offer the allure of high performance while sacrificing flexibility.
no code implementations • 17 Apr 2019 • Jared Roesch, Steven Lyubomirsky, Marisa Kirisame, Logan Weber, Josh Pollock, Luis Vega, Ziheng Jiang, Tianqi Chen, Thierry Moreau, Zachary Tatlock
Using these extension mechanisms, Relay supports a unified compiler that can target a variety of hardware platforms.
1 code implementation • 8 Feb 2020 • Ziheng Jiang, Chiyuan Zhang, Kunal Talwar, Michael C. Mozer
We obtain empirical estimates of this score for individual instances in multiple data sets, and we show that the score identifies out-of-distribution and mislabeled examples at one end of the continuum and strongly regular examples at the other end.
1 code implementation • 5 Oct 2020 • Xin Liu, Ziheng Jiang, Josh Fromm, Xuhai Xu, Shwetak Patel, Daniel McDuff
There are large individual differences in physiological processes, making designing personalized health sensing algorithms challenging.
no code implementations • 20 Jan 2021 • Xin Liu, Yuang Li, Josh Fromm, Yuntao Wang, Ziheng Jiang, Alex Mariakakis, Shwetak Patel
In this work, we demonstrate state-of-the-art latency and accuracy for on-device super-resolution using a novel hybrid architecture called SplitSR and a novel lightweight residual block called SplitSRBlock.
no code implementations • 27 Mar 2021 • Ziheng Jiang, Animesh Jain, Andrew Liu, Josh Fromm, Chengqian Ma, Tianqi Chen, Luis Ceze
Quantization is a key technique to reduce the resource requirement and improve the performance of neural network deployment.
no code implementations • 29 Sep 2021 • Xin Liu, Brian L. Hill, Ziheng Jiang, Shwetak Patel, Daniel McDuff
Camera-based physiological measurement is a growing field with neural models providing state-the-art-performance.
no code implementations • 9 Oct 2021 • Xin Liu, Brian L. Hill, Ziheng Jiang, Shwetak Patel, Daniel McDuff
Camera-based physiological measurement is a growing field with neural models providing state-the-art-performance.
Face Detection Photoplethysmography (PPG) heart rate estimation
no code implementations • 11 Mar 2022 • Xin Liu, Mingchuan Zhang, Ziheng Jiang, Shwetak Patel, Daniel McDuff
The growing need for technology that supports remote healthcare is being acutely highlighted by an aging population and the COVID-19 pandemic.
no code implementations • 1 Nov 2023 • Ruihang Lai, Junru Shao, Siyuan Feng, Steven S. Lyubomirsky, Bohan Hou, Wuwei Lin, Zihao Ye, Hongyi Jin, Yuchen Jin, Jiawei Liu, Lesheng Jin, Yaxing Cai, Ziheng Jiang, Yong Wu, Sunghyun Park, Prakalp Srivastava, Jared G. Roesch, Todd C. Mowry, Tianqi Chen
Dynamic shape computations have become critical in modern machine learning workloads, especially in emerging large language models.
1 code implementation • 23 Feb 2024 • Ziheng Jiang, Haibin Lin, Yinmin Zhong, Qi Huang, Yangrui Chen, Zhi Zhang, Yanghua Peng, Xiang Li, Cong Xie, Shibiao Nong, Yulu Jia, Sun He, Hongmin Chen, Zhihao Bai, Qi Hou, Shipeng Yan, Ding Zhou, Yiyao Sheng, Zhuo Jiang, Haohan Xu, Haoran Wei, Zhang Zhang, Pengfei Nie, Leqi Zou, Sida Zhao, Liang Xiang, Zherui Liu, Zhe Li, Xiaoying Jia, Jianxi Ye, Xin Jin, Xin Liu
Training LLMs at this scale brings unprecedented challenges to training efficiency and stability.