Low-Power Computer Vision

On device AI has become increasingly important for reasons of latency, privacy and overall autonomy as computing becomes more and more ambient. Moreover, making AI, in particular computer vision, efficient and run well in low resource computing environments using frameworks like PyTorch is a priority of the industry to enable this. The IEEE Low-Power Computer Vision Challenge is one such effort that has and continues to push the field forward allowing us to make progress in this area. Facebook has been a proud sponsor and supporter of this challenge since 2018 and this book presents the winners' solutions from previous challenges and can guide researchers, engineers, and students to design efficient on device AI.
-- Joe Spisak, Product Lead at Facebook Artificial Intelligence

Computer vision is at the center of recent breakthroughs in artificial intelligence. Being able to process visual data in low-power computing environments will enable great advances in the field in areas such as edge computing and Internet of Things. This book presents work by experts in the field and their winning solutions. It is an indispensable resource for anyone interested creating AI technologies in resource constrained computing environments
-- Mark Liao, Director, Institute of Information Science, Academia Sinica

From mobile phones to wearable health monitors, improved energy efficiency is the enabling technology of everything we take for granted today. Computer vision is at the center of artificial intelligence and machine learning. Today, artificial intelligence and low power are often at different ends of the spectrum. Low-power computer vision will enable greater adoption of the technologies in battery-powered IoT (Internet of Things) systems. This book collects the winners' solutions of the Low-Power Computer Vision Challenge and provides insight on how to improve efficiency of artificial intelligence.
-- Edwin Park, Principal Engineer at Qualcomm