no code implementations • 23 May 2023 • Yuting Wu, Qiwen Wang, Ziyu Wang, Xinxin Wang, Buvna Ayyagari, Siddarth Krishnan, Michael Chudzik, Wei D. Lu
The efficacy of training larger models is evaluated using realistic hardware parameters and shows that that analog CIM modules can enable efficient mix-precision DNN training with accuracy comparable to full-precision software trained models.
no code implementations • 13 Apr 2023 • Ziyu Wang, Yuting Wu, Yongmo Park, Sangmin Yoo, Xinxin Wang, Jason K. Eshraghian, Wei D. Lu
Analog compute-in-memory (CIM) systems are promising for deep neural network (DNN) inference acceleration due to their energy efficiency and high throughput.
no code implementations • 19 Mar 2023 • Sangmin Yoo, Eric Yeu-Jer Lee, Ziyu Wang, Xinxin Wang, Wei D. Lu
Event-based cameras are inspired by the sparse and asynchronous spike representation of the biological visual system.
no code implementations • 10 Feb 2023 • Ben Chen, Linbo Jin, Xinxin Wang, Dehong Gao, Wen Jiang, Wei Ning
Same-style products retrieval plays an important role in e-commerce platforms, aiming to identify the same products which may have different text descriptions or images.
2 code implementations • 4 Nov 2022 • Xinxin Wang, Guanzhong Wang, Qingqing Dang, Yi Liu, Xiaoguang Hu, dianhai yu
With multi-scale training and testing, PP-YOLOE-R-l and PP-YOLOE-R-x further improve the detection precision to 80. 02 and 80. 73 mAP.
Ranked #6 on Oriented Object Detection on DOTA 1.0
8 code implementations • 30 Mar 2022 • Shangliang Xu, Xinxin Wang, Wenyu Lv, Qinyao Chang, Cheng Cui, Kaipeng Deng, Guanzhong Wang, Qingqing Dang, Shengyu Wei, Yuning Du, Baohua Lai
In this report, we present PP-YOLOE, an industrial state-of-the-art object detector with high performance and friendly deployment.
Ranked #1 on Object Detection on BDD100K val
3 code implementations • 27 Sep 2021 • Jason K. Eshraghian, Max Ward, Emre Neftci, Xinxin Wang, Gregor Lenz, Girish Dwivedi, Mohammed Bennamoun, Doo Seok Jeong, Wei D. Lu
This paper serves as a tutorial and perspective showing how to apply the lessons learnt from several decades of research in deep learning, gradient descent, backpropagation and neuroscience to biologically plausible spiking neural neural networks.
1 code implementation • 21 Apr 2021 • Xin Huang, Xinxin Wang, Wenyu Lv, Xiaying Bai, Xiang Long, Kaipeng Deng, Qingqing Dang, Shumin Han, Qiwen Liu, Xiaoguang Hu, dianhai yu, Yanjun Ma, Osamu Yoshie
To meet these two concerns, we comprehensively evaluate a collection of existing refinements to improve the performance of PP-YOLO while almost keep the infer time unchanged.
no code implementations • 31 Mar 2021 • Jian Guan, Wenbo Wang, Pengming Feng, Xinxin Wang, Wenwu Wang
However, the high-dimensional embedding obtained via 1-D convolution and positional encoding can lead to the loss of the signal's own temporal information and a large amount of training parameters.