Real-time machine vision based on AI and ML approaches is a critical technology for autonomous vehicles, smart cities, and industrial computer vision. Advances in 3D VLSI allow us to integrate sensing, computation, and memory in a single platform that provides much higher performance and lower power than is possible even with traditional embedded computer vision platforms. AI and machine learning methods can be tightly integrated with sensing to create small, low-power, high-performance embedded machine vision systems. However, the realization of these systems will require co-design with innovations for both algorithms and architectures.

Potential topics include:

• Analog and mixed-signal on-sensor computation.
• Digital on-sensor computation.
• 3D VLSI architectures for embedded AI.
• Computer vision applications (tracking, target identification, etc.) adapted to a chosen real-time AI vision platform.
• Algorithm-architecture co-design for embedded AI vision.
• Simulation studies of real-time embedded AI vision systems.
• Benchmarks for evaluation of embedded AI vision systems

Submission Instructions

Submit a 4-8 page extended abstract or paper to emai2019bmvc@gmail.com

using the BMVC19 format. Blind submission is not required.

Important Dates

Submissions (Extended): Monday, July 8, 2019

Acceptance notification: Monday, July 29, 2019

Final ‘camera-ready’ versions: Monday, August 12th, 2019

Organizing Committee

Prof. Saibal Mukhopodhyay, Georgia Tech, saibal@ece.gatech.edu

Prof. Marilyn Wolf, Georgia Tech, wolf@ece.gatech.edu

Technical Program Committee

Prof. Saibal Mukhopodhyay, Georgia Tech.

Prof. Marilyn Wolf, Georgia Tech.

Dr. Anthony Griffin, Auckland University of Technology.

Prof. Senem Velipasalar, Syracuse University.

Prof. Bernhard Rinner, University of Klagenfurt.

Prof. Christophe Bobda, University of Florida.

Prof. Francois Berry, Université Clermont Auvergne