图 书 馆
基于FPGA的全球土地覆盖分类Planet-Scale Land Cover Classification With FPGAs |
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| 课程网址: | http://videolectures.net/kdd2018_sirosh_cover_classification/ |
| 主讲教师: | Joseph Sirosh |
| 开课单位: | 微软 |
| 开课时间: | 2018-11-23 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | 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. |
| 关 键 词: | AI for Earth; 微软的云和AI工具; 国家农业图像计划; 现场可编程阵列 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2023-01-28：cyh |
| 最后编审: | 2023-01-28：cyh |
| 阅读次数: | 39 |