no code implementations • 30 Nov 2020 • Hsin-Pai Cheng, Feng Liang, Meng Li, Bowen Cheng, Feng Yan, Hai Li, Vikas Chandra, Yiran Chen
We use ScaleNAS to create high-resolution models for two different tasks, ScaleNet-P for human pose estimation and ScaleNet-S for semantic segmentation.
Ranked #5 on Multi-Person Pose Estimation on COCO test-dev
no code implementations • 8 Jul 2020 • Hsin-Pai Cheng, Tunhou Zhang, Yixing Zhang, Shi-Yu Li, Feng Liang, Feng Yan, Meng Li, Vikas Chandra, Hai Li, Yiran Chen
To preserve graph correlation information in encoding, we propose NASGEM which stands for Neural Architecture Search via Graph Embedding Method.
no code implementations • 4 Mar 2020 • Byung Hoon Ahn, Jinwon Lee, Jamie Menjay Lin, Hsin-Pai Cheng, Jilei Hou, Hadi Esmaeilzadeh
To address this standing issue, we present a memory-aware compiler, dubbed SERENITY, that utilizes dynamic programming to find a sequence that finds a schedule with optimal memory footprint.
1 code implementation • 21 Nov 2019 • Tunhou Zhang, Hsin-Pai Cheng, Zhenwen Li, Feng Yan, Chengyu Huang, Hai Li, Yiran Chen
Specifically, both ShrinkCNN and ShrinkRNN are crafted within 1. 5 GPU hours, which is 7. 2x and 6. 7x faster than the crafting time of SOTA CNN and RNN models, respectively.
1 code implementation • 19 Jun 2019 • Hsin-Pai Cheng, Tunhou Zhang, Yukun Yang, Feng Yan, Shi-Yu Li, Harris Teague, Hai Li, Yiran Chen
Designing neural architectures for edge devices is subject to constraints of accuracy, inference latency, and computational cost.
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 • 6 Dec 2018 • Jingyang Zhang, Hsin-Pai Cheng, Chunpeng Wu, Hai Li, Yiran Chen
We intuitively and empirically prove the rationality of our method in reducing the search space.
no code implementations • 27 Nov 2018 • Hsin-Pai Cheng, Patrick Yu, Haojing Hu, Feng Yan, Shi-Yu Li, Hai Li, Yiran Chen
Distributed learning systems have enabled training large-scale models over large amount of data in significantly shorter time.
1 code implementation • NIPS Workshop CDNNRIA 2018 • Hsin-Pai Cheng, Yuanjun Huang, Xuyang Guo, Yifei HUANG, Feng Yan, Hai Li, Yiran Chen
Thus judiciously selecting different precision for different layers/structures can potentially produce more efficient models compared to traditional quantization methods by striking a better balance between accuracy and compression rate.
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 • 27 May 2017 • Chang Song, Hsin-Pai Cheng, Huanrui Yang, Sicheng Li, Chunpeng Wu, Qing Wu, Hai Li, Yiran Chen
Our experiments show that different adversarial strengths, i. e., perturbation levels of adversarial examples, have different working zones to resist the attack.