Wikipedia: Harnessing Collaborative Intelligence

2013 ◽  
pp. 127-138 ◽  
Author(s):  
Zann Gill
Keyword(s):  
Collaborative Intelligence

scholarly journals Lightweight Compression of Intermediate Neural Network Features for Collaborative Intelligence

2021 ◽  
Vol 2 ◽  
pp. 350-362
Author(s):  
Robert A. Cohen ◽  
Hyomin Choi ◽  
Ivan V. Bajic
Keyword(s):  
Neural Network ◽  
Collaborative Intelligence

scholarly journals Guest editorial: Collaborative intelligence for vehicular Internet of Things

2021 ◽  
Vol 18 (7) ◽  
pp. iii-vi
Author(s):  
Celimuge Wu ◽  
Kok-Lim Alvin Yau ◽  
Carlos Tavares Calafate ◽  
Lei Zhong
Keyword(s):  
Internet Of Things ◽  
Guest Editorial ◽  
Collaborative Intelligence

Collaborative Intelligence

2019 ◽  
pp. 73-80
Author(s):  
Sabine Flores
Keyword(s):  
Collaborative Intelligence

scholarly journals Collaborative Intelligence: Accelerating Deep Neural Network Inference via Device-Edge Synergy

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Nanliang Shan ◽  
Zecong Ye ◽  
Xiaolong Cui
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Deep Neural Network ◽  
Network Inference ◽  
Partition Scheme ◽  
Model Inference ◽  
Cloud Server ◽  
Hardware Configuration ◽  
Collaborative Intelligence ◽  
Service Latency

With the development of mobile edge computing (MEC), more and more intelligent services and applications based on deep neural networks are deployed on mobile devices to meet the diverse and personalized needs of users. Unfortunately, deploying and inferencing deep learning models on resource-constrained devices are challenging. The traditional cloud-based method usually runs the deep learning model on the cloud server. Since a large amount of input data needs to be transmitted to the server through WAN, it will cause a large service latency. This is unacceptable for most current latency-sensitive and computation-intensive applications. In this paper, we propose Cogent, an execution framework that accelerates deep neural network inference through device-edge synergy. In the Cogent framework, it is divided into two operation stages, including the automatic pruning and partition stage and the containerized deployment stage. Cogent uses reinforcement learning (RL) to automatically predict pruning and partition strategies based on feedback from the hardware configuration and system conditions so that the pruned and partitioned model can better adapt to the system environment and user hardware configuration. Then through containerized deployment to the device and the edge server to accelerate model inference, experiments show that the learning-based hardware-aware automatic pruning and partition scheme can significantly reduce the service latency, and it accelerates the overall model inference process while maintaining accuracy. Using this method can accelerate up to 8.89× without loss of accuracy of more than 7%.

Deepening e-Learning through Social-Collaborative Intelligence

2018 ◽  
Author(s):  
Matthew Montebello ◽  
Bill Cope ◽  
Mary Kalantzis ◽  
Tabassum Amina ◽  
Duane Searsmith ◽  
...  
Keyword(s):  
E Learning ◽  
Collaborative Intelligence

Smart Sensing for HVAC Control: Collaborative Intelligence in Optical and IR Cameras

2018 ◽  
Vol 65 (12) ◽  
pp. 9785-9794 ◽  
Author(s):  
Ningyuan Cao ◽  
Justin Ting ◽  
Shreyas Sen ◽  
Arijit Raychowdhury
Keyword(s):  
Hvac Control ◽  
Smart Sensing ◽  
Collaborative Intelligence
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