New NSF Grant: Advancing Low-Power Computer Vision at the Edge

George K. Thiruvathukal and Neil Klingensmith receive a $250,000 grant from the National Science Foundation to support computer vision at the edge.

New NSF Grant: Advancing Low-Power Computer Vision at the Edge
A hierarchical neural network that divides and distributes CV processing in edge networks.

This proposal enables low-power edge computers, such as mobile phones, drones, and Internet-of-Things devices, to benefit society. Computer vision is the technology to automatically analyze images and videos. Computer vision on these devices can keep humans safe, for example by spotting dangers in a factory or at a construction site.

This project addresses two challenges that hamper practical adoption of computer vision on edge devices.

  • The first challenge is that current computer vision approaches require powerful computers, but these computers are too far away and have long response time. This project brings the computers to the places where data is acquired. The project makes computer vision more efficient, so that visual data can be analyzed by small edge devices like phones and drones.
  • The second challenge is that building complex software for computer vision is difficult. This project provides software engineering support for emerging computer vision technologies. As a result of addressing these two challenges, computer vision on the edge can become feasible.

For additional details, see NSF Award Abstract #2107020.