Soldiers on the battlefield are not able to rely on high-powered bulky devices or the cloud to conduct operations, so how can they efficiently run the programs and algorithms needed to be successful in their missions?
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A collaborative effort between Army researchers has resulted in a tool that will enable the Army to model, characterize and predict the performance of current and future machine learning-based applications on mobile devices, enabling the deployment of advanced analytics to the tactical edge to support Command, Control, Communications, Computers, Intelligence, Surveillance and Reconnaissance operations.
This research is being conducted by Dr. Kevin Chan from the U.S. Army Combat Capabilities Development Command’s Army Research Laboratory, Pennsylvania State University and IBM, a collaborative effort made possible by the lab’s Network Science Collaborative Technology Alliance that is slated to conclude this year after a 10-year run.
The researchers detail their achievements in papers recently accepted to the Institute of Electrical and Electronics Engineers Transactions on Mobile Computing titled Augur: Modeling the Resource Requirements of ConvNets on Mobile Devices and to the IEEE/ACM Transactions on Networking titled NetVision: On-demand Video Processing in Wireless Networks.
This research studies how convolutional neural networks on mobile devices such as smartphones are being used for various applications like object detection, language translation and audio classification, Chan said.
“Given the rapid advances and development of artificial intelligence and machine learning techniques, most of the research in deep learning is studied using devices or platforms that have a lot more resources to include processing, energy and storage, and commercial applications use the cloud for some of these complex computations,” Chan said. “As a result, there’s a great deal of uncertainty in the performance and resource requirements of these algorithms on mobile devices, for instance if they’ll take forever to run or use up all of the battery.”
The researchers profiled several different commonly used deep learning algorithms on numerous different current mobile computing platforms, including smartphones and mobile graphics processing units, and characterized how they performed.
The primary collaborator of this work was Professor Thomas La Porta, director, School of Electrical Engineering and Computer Science, and Evan Pugh Professor and William E. Leonhard Professor at Pennsylvania State University.
“We characterized the runtime, memory usage and energy usage of these platforms, whereas typical studies are concerned with runtime and performance,” La Porta said. “The edge analytics requires us to study how these algorithms work on mobile devices. Obviously, commercial applications and vendors are interested in having applications work on smartphones, but they can more readily go to the cloud for help.”
With this, the researchers developed a tool called Augur that is able to predict the performance and resource usage of future algorithms on future mobile devices.