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20 results on '"Wang, Taoyi"'

5. Wireless multilateral devices for optogenetic studies of individual and social behaviors

Author

Yang, Yiyuan, Wu, Mingzheng, Vázquez-Guardado, Abraham, Wegener, Amy J., Grajales-Reyes, Jose G., Deng, Yujun, Wang, Taoyi, Avila, Raudel, Moreno, Justin A., Minkowicz, Samuel, Dumrongprechachan, Vasin, Lee, Jungyup, Zhang, Shuangyang, Legaria, Alex A., Ma, Yuhang, Mehta, Sunita, Franklin, Daniel, Hartman, Layne, Bai, Wubin, Han, Mengdi, Zhao, Hangbo, Lu, Wei, Yu, Yongjoon, Sheng, Xing, Banks, Anthony, Yu, Xinge, Donaldson, Zoe R., Gereau, IV, Robert W., Good, Cameron H., Xie, Zhaoqian, Huang, Yonggang, Kozorovitskiy, Yevgenia, and Rogers, John A.

Published
2021
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6. Author Correction: Wireless multilateral devices for optogenetic studies of individual and social behaviors

Author

Yang, Yiyuan, Wu, Mingzheng, Vázquez-Guardado, Abraham, Wegener, Amy J., Grajales-Reyes, Jose G., Deng, Yujun, Wang, Taoyi, Avila, Raudel, Moreno, Justin A., Minkowicz, Samuel, Dumrongprechachan, Vasin, Lee, Jungyup, Zhang, Shuangyang, Legaria, Alex A., Ma, Yuhang, Mehta, Sunita, Franklin, Daniel, Hartman, Layne, Bai, Wubin, Han, Mengdi, Zhao, Hangbo, Lu, Wei, Yu, Yongjoon, Sheng, Xing, Banks, Anthony, Yu, Xinge, Donaldson, Zoe R., Gereau, IV, Robert W., Good, Cameron H., Xie, Zhaoqian, Huang, Yonggang, Kozorovitskiy, Yevgenia, and Rogers, John A.

Published
2022
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9. HASP: Hierarchical Asynchronous Parallelism for Multi-NN Tasks

Author

Li, Hongyi, Ma, Songchen, Wang, Taoyi, Zhang, Weihao, Wang, Guanrui, Song, Chenhang, Qu, Huanyu, Lin, Junfeng, Ma, Cheng, Pei, Jing, and Zhao, Rong

Abstract

The rapid development of deep learning has propelled many real-world artificial intelligence applications. Many of these applications integrate multiple neural networks (multi-NN) to cater to various functionalities. There are two challenges of multi-NN acceleration: (1) competition for shared resources becomes a bottleneck, and (2) heterogeneous workloads exhibit remarkably different computing-memory characteristics and various synchronization requirements. Therefore, resource isolation and fine-grained resource allocation for each task are two fundamental requirements for multi-NN computing systems. Although a number of multi-NN acceleration technologies have been explored, few can completely fulfill both of these requirements, especially for mobile scenarios. This paper reports a Hierarchical Asynchronous Parallel Model (HASP) to enhance multi-NN performance to meet both requirements. HASP can be implemented on a multicore processor that adopts Multiple Instruction Multiple Data (MIMD) or Single Instruction Multiple Thread (SIMT) architectures, with minor adaptive modification needed. Further, a prototype chip is developed to validate the hardware effectiveness of this design. A corresponding mapping strategy is also developed, allowing the proposed architecture to simultaneously promote resource utilization and throughput. With the same workload, the prototype chip demonstrates 3.62$\boldsymbol{\times}$×, and 3.51$\boldsymbol{\times}$× higher throughput over Planaria and 8.68$\boldsymbol{\times}$×, 2.61$\boldsymbol{\times}$× over Jetson AGX Orin for MobileNet-V1 and ResNet50, respectively.

Published
2024
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11. Neuromorphic computing chip with spatiotemporal elasticity for multi-intelligent-tasking robots

Author

Ma, Songchen, primary, Pei, Jing, additional, Zhang, Weihao, additional, Wang, Guanrui, additional, Feng, Dahu, additional, Yu, Fangwen, additional, Song, Chenhang, additional, Qu, Huanyu, additional, Ma, Cheng, additional, Lu, Mingsheng, additional, Liu, Faqiang, additional, Zhou, Wenhao, additional, Wu, Yujie, additional, Lin, Yihan, additional, Li, Hongyi, additional, Wang, Taoyi, additional, Song, Jiuru, additional, Liu, Xue, additional, Li, Guoqi, additional, Zhao, Rong, additional, and Shi, Luping, additional

Published
2022
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16. A wireless, implantable optoelectrochemical probe for optogenetic stimulation and dopamine detection

Author

Liu, Changbo, primary, Zhao, Yu, additional, Cai, Xue, additional, Xie, Yang, additional, Wang, Taoyi, additional, Cheng, Dali, additional, Li, Lizhu, additional, Li, Rongfeng, additional, Deng, Yuping, additional, Ding, He, additional, Lv, Guoqing, additional, Zhao, Guanlei, additional, Liu, Lei, additional, Zou, Guisheng, additional, Feng, Meixin, additional, Sun, Qian, additional, Yin, Lan, additional, and Sheng, Xing, additional

Published
2020
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19. The Design and Implementation of Cardiotocography Signals Classification Algorithm Based on Neural Network.

Author

Tang, Haijing, Wang, Taoyi, Li, Mengke, and Yang, Xu

Subjects
*FETAL heart rate monitoring, *SIGNAL classification, *CLASSIFICATION algorithms, *NEURAL computers, *FETAL development
Abstract

Mobile medical care is a hot issue in current medical research. Due to the inconvenience of going to hospital for fetal heart monitoring and the limited medical resources, real-time monitoring of fetal health on portable devices has become an urgent need for pregnant women, which helps to protect the health of the fetus in a more comprehensive manner and reduce the workload of doctors. For the feature acquisition of the fetal heart rate (FHR) signal, the traditional feature-based classification methods need to manually read the morphological features from the FHR curve, which is time-consuming and costly and has a certain degree of calibration bias. This paper proposes a classification method of the FHR signal based on neural networks, which can avoid manual feature acquisition and reduce the error caused by human factors. The algorithm will directly learn from the FHR data and truly realize the real-time diagnosis of FHR data. The convolution neural network classification method named "MKNet" and recurrent neural network named "MKRNN" are designed. The main contents of this paper include the preprocessing of the FHR signal, the training of the classification model, and the experiment evaluation. Finally, MKNet is proved to be the best algorithm for real-time FHR signal classification. [ABSTRACT FROM AUTHOR]

Published
2018
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