no code implementations • 14 Jun 2022 • Yukun Bao, Liang Shen, Xiaoyuan Zhang, Yanmei Huang, Changrui Deng
Electricity consumption forecasting has vital importance for the energy planning of a country.
1 code implementation • 20 May 2022 • Liang Shen, Zhihua Wu, Weibao Gong, Hongxiang Hao, Yangfan Bai, HuaChao Wu, Xinxuan Wu, Jiang Bian, Haoyi Xiong, dianhai yu, Yanjun Ma
With the increasing diversity of ML infrastructures nowadays, distributed training over heterogeneous computing systems is desired to facilitate the production of big models.
no code implementations • 10 Mar 2022 • Shaojie Zhuo, Hongyu Chen, Ramchalam Kinattinkara Ramakrishnan, Tommy Chen, Chen Feng, Yicheng Lin, Parker Zhang, Liang Shen
In this study, we focus on post-training quantization (PTQ) algorithms that quantize a model to low-bit (less than 8-bit) precision with only a small set of calibration data and benchmark them on different tinyML use cases.
1 code implementation • 6 Dec 2021 • Yulong Ao, Zhihua Wu, dianhai yu, Weibao Gong, Zhiqing Kui, Minxu Zhang, Zilingfeng Ye, Liang Shen, Yanjun Ma, Tian Wu, Haifeng Wang, Wei Zeng, Chao Yang
The experiments demonstrate that our framework can satisfy various requirements from the diversity of applications and the heterogeneity of resources with highly competitive performance.
no code implementations • 31 May 2020 • Xiaotao Huang, Liang Shen, Chongyi Fan, Jiahua zhu, Sixian Chen
Though effective in the segmentation, conventional multilevel thresholding methods are computationally expensive as exhaustive search are used for optimal thresholds to optimize the objective functions.
no code implementations • 26 Oct 2019 • Liang Shen, Jiahua zhu, Chongyi Fan, Xiaotao Huang, Tian Jin
In this paper, we develop a novel method considering all the feature center position coordinates, the local feature shape and orientation information based on Gaussian Mixture Model for co-variant feature matching.
no code implementations • 15 Apr 2019 • Sergei Alyamkin, Matthew Ardi, Alexander C. Berg, Achille Brighton, Bo Chen, Yiran Chen, Hsin-Pai Cheng, Zichen Fan, Chen Feng, Bo Fu, Kent Gauen, Abhinav Goel, Alexander Goncharenko, Xuyang Guo, Soonhoi Ha, Andrew Howard, Xiao Hu, Yuanjun Huang, Donghyun Kang, Jaeyoun Kim, Jong Gook Ko, Alexander Kondratyev, Junhyeok Lee, Seungjae Lee, Suwoong Lee, Zichao Li, Zhiyu Liang, Juzheng Liu, Xin Liu, Yang Lu, Yung-Hsiang Lu, Deeptanshu Malik, Hong Hanh Nguyen, Eunbyung Park, Denis Repin, Liang Shen, Tao Sheng, Fei Sun, David Svitov, George K. Thiruvathukal, Baiwu Zhang, Jingchi Zhang, Xiaopeng Zhang, Shaojie Zhuo
In addition to mobile phones, many autonomous systems rely on visual data for making decisions and some of these systems have limited energy (such as unmanned aerial vehicles also called drones and mobile robots).
no code implementations • 12 Mar 2019 • Chen Feng, Tao Sheng, Zhiyu Liang, Shaojie Zhuo, Xiaopeng Zhang, Liang Shen, Matthew Ardi, Alexander C. Berg, Yiran Chen, Bo Chen, Kent Gauen, Yung-Hsiang Lu
The IEEE Low-Power Image Recognition Challenge (LPIRC) is an annual competition started in 2015 that encourages joint hardware and software solutions for computer vision systems with low latency and power.
no code implementations • 3 Oct 2018 • Sergei Alyamkin, Matthew Ardi, Achille Brighton, Alexander C. Berg, Yiran Chen, Hsin-Pai Cheng, Bo Chen, Zichen Fan, Chen Feng, Bo Fu, Kent Gauen, Jongkook Go, Alexander Goncharenko, Xuyang Guo, Hong Hanh Nguyen, Andrew Howard, Yuanjun Huang, Donghyun Kang, Jaeyoun Kim, Alexander Kondratyev, Seungjae Lee, Suwoong Lee, Junhyeok Lee, Zhiyu Liang, Xin Liu, Juzheng Liu, Zichao Li, Yang Lu, Yung-Hsiang Lu, Deeptanshu Malik, Eunbyung Park, Denis Repin, Tao Sheng, Liang Shen, Fei Sun, David Svitov, George K. Thiruvathukal, Baiwu Zhang, Jingchi Zhang, Xiaopeng Zhang, Shaojie Zhuo
The Low-Power Image Recognition Challenge (LPIRC, https://rebootingcomputing. ieee. org/lpirc) is an annual competition started in 2015.
no code implementations • 17 Sep 2018 • Liang Shen, Dengming Zhu, Saad Nadeem, Zhaoqi Wang, Arie Kaufman
The approach includes an economical data capture technique using inexpensive CCD cameras.
1 code implementation • 22 Mar 2018 • Tao Sheng, Chen Feng, Shaojie Zhuo, Xiaopeng Zhang, Liang Shen, Mickey Aleksic
As deep learning (DL) is being rapidly pushed to edge computing, researchers invented various ways to make inference computation more efficient on mobile/IoT devices, such as network pruning, parameter compression, and etc.
no code implementations • 21 Jan 2018 • Liang Shen, Zihan Yue, Quan Chen, Fan Feng, Jie Ma
On the other hand, the accumulation of rain streaks from long distance makes the rain image look like haze veil.
no code implementations • 7 Nov 2017 • Liang Shen, Zihan Yue, Fan Feng, Quan Chen, Shihao Liu, Jie Ma
In this paper, a low-light image enhancement model based on convolutional neural network and Retinex theory is proposed.