Planet-Scale Land Cover Classification With FPGAs
published: Sept. 24, 2018, recorded: August 2018, views: 691
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
AI for Earth puts Microsoft’s cloud and AI tools in the hands of those working to solve global environmental challenges. Land cover mapping is part of Microsoft’s AI for Earth program, which was created in order to fundamentally change the way that society monitors, models, and ultimately manages Earth’s natural resources. To power the land cover mapping work, DNNs are used to perform land use classification using tens of terabytes of high-resolution satellite images from National Agriculture Imagery Program (NAIP). However, Deep Neural Networks (DNNs) are challenging to infer cost-effectively, and deploy in large-scale online services with low latencies and price/performance. Microsoft Project Brainwave is a hardware architecture designed to enable high performance real-time AI computations, and the architecture is deployed on field programmable arrays (FPGAs). This wave of hardware innovation will fundamentally transform latencies and price-performance for large scale use of DNNs. In this session, we will walkthrough how FPGAs are used within Microsoft, and how we can tap the power of FPGAs for real-time AI. We will share the secrets of how we are able to perform land cover classification on 20 terabytes of high-resolutions satellite images from NAIP in ten minutes, at the rate of over 415,000 inferences/second.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !