Zobrazeno 1 - 10
of 22
pro vyhledávání: '"Tung, Caleb"'
Autor:
Tung, Caleb, Eliopoulos, Nicholas, Jajal, Purvish, Ramshankar, Gowri, Yang, Chen-Yun, Synovic, Nicholas, Zhang, Xuecen, Chaudhary, Vipin, Thiruvathukal, George K., Lu, Yung-Hsiang
Computer vision often uses highly accurate Convolutional Neural Networks (CNNs), but these deep learning models are associated with ever-increasing energy and computation requirements. Producing more energy-efficient CNNs often requires model trainin
Externí odkaz:
http://arxiv.org/abs/2310.07782
Autor:
Bhardwaj, Kartikeya, Ward, James, Tung, Caleb, Gope, Dibakar, Meng, Lingchuan, Fedorov, Igor, Chalfin, Alex, Whatmough, Paul, Loh, Danny
Is it possible to restructure the non-linear activation functions in a deep network to create hardware-efficient models? To address this question, we propose a new paradigm called Restructurable Activation Networks (RANs) that manipulate the amount o
Externí odkaz:
http://arxiv.org/abs/2208.08562
Autor:
Tung, Caleb, Goel, Abhinav, Bordwell, Fischer, Eliopoulos, Nick, Hu, Xiao, Thiruvathukal, George K., Lu, Yung-Hsiang
Object detectors are vital to many modern computer vision applications. However, even state-of-the-art object detectors are not perfect. On two images that look similar to human eyes, the same detector can make different predictions because of small
Externí odkaz:
http://arxiv.org/abs/2207.13890
Autor:
Tung, Caleb, Goel, Abhinav, Hu, Xiao, Eliopoulos, Nicholas, Amobi, Emmanuel, Thiruvathukal, George K., Chaudhary, Vipin, Lu, Yung-Hsiang
Computer vision is often performed using Convolutional Neural Networks (CNNs). CNNs are compute-intensive and challenging to deploy on power-contrained systems such as mobile and Internet-of-Things (IoT) devices. CNNs are compute-intensive because th
Externí odkaz:
http://arxiv.org/abs/2207.10741
Autor:
Goel, Abhinav, Tung, Caleb, Hu, Xiao, Thiruvathukal, George K., Davis, James C., Lu, Yung-Hsiang
Computer vision on low-power edge devices enables applications including search-and-rescue and security. State-of-the-art computer vision algorithms, such as Deep Neural Networks (DNNs), are too large for inference on low-power edge devices. To impro
Externí odkaz:
http://arxiv.org/abs/2109.13356
Autor:
Goel, Abhinav, Tung, Caleb, Hu, Xiao, Wang, Haobo, Davis, James C., Thiruvathukal, George K., Lu, Yung-Hsiang
Low-power computer vision on embedded devices has many applications. This paper describes a low-power technique for the object re-identification (reID) problem: matching a query image against a gallery of previously seen images. State-of-the-art tech
Externí odkaz:
http://arxiv.org/abs/2106.10588
Autor:
Ghodgaonkar, Isha, Chakraborty, Subhankar, Banna, Vishnu, Allcroft, Shane, Metwaly, Mohammed, Bordwell, Fischer, Kimura, Kohsuke, Zhao, Xinxin, Goel, Abhinav, Tung, Caleb, Chinnakotla, Akhil, Xue, Minghao, Lu, Yung-Hsiang, Ward, Mark Daniel, Zakharov, Wei, Ebert, David S., Barbarash, David M., Thiruvathukal, George K.
In order to contain the COVID-19 pandemic, countries around the world have introduced social distancing guidelines as public health interventions to reduce the spread of the disease. However, monitoring the efficacy of these guidelines at a large sca
Externí odkaz:
http://arxiv.org/abs/2008.12363
Autor:
Goel, Abhinav, Tung, Caleb, Aghajanzadeh, Sara, Ghodgaonkar, Isha, Ghosh, Shreya, Thiruvathukal, George K., Lu, Yung-Hsiang
Deep Neural Networks (DNNs) can achieve state-of-the-art accuracy in many computer vision tasks, such as object counting. Object counting takes two inputs: an image and an object query and reports the number of occurrences of the queried object. To a
Externí odkaz:
http://arxiv.org/abs/2007.01369
Autor:
Ghodgaonkar, Isha, Goel, Abhinav, Bordwell, Fischer, Tung, Caleb, Aghajanzadeh, Sara, Curran, Noah, Chen, Ryan, Yu, Kaiwen, Mahapatra, Sneha, Banna, Vishnu, Kao, Gore, Lee, Kate, Hu, Xiao, Eliopolous, Nick, Chinnakotla, Akhil, Rijhwani, Damini, Kim, Ashley, Chakraborty, Aditya, Ward, Mark Daniel, Lu, Yung-Hsiang, Thiruvathukal, George K.
COVID-19 has resulted in a worldwide pandemic, leading to "lockdown" policies and social distancing. The pandemic has profoundly changed the world. Traditional methods for observing these historical events are difficult because sending reporters to a
Externí odkaz:
http://arxiv.org/abs/2005.09091
Deep neural networks (DNNs) are successful in many computer vision tasks. However, the most accurate DNNs require millions of parameters and operations, making them energy, computation and memory intensive. This impedes the deployment of large DNNs i
Externí odkaz:
http://arxiv.org/abs/2003.11066