City-Scale Edge Inference, Built in 3 Weeks

April 24, 2019
Video surveillance is growing at a breakneck pace, creating urgent demand for automated analytics. Deploying hundreds to thousands of cameras results in far too many video streams for humans to monitor. To help cities make better use of this data, cloud-based AI solutions are coming to the rescue. 
 
In this article, IoT engineers will learn:
 
Why deep learning at the edge enables more powerful analytics in the cloud
How the OpenVINOTM Toolkit enables deep learning and accelerates edge surveillance
About tuning video analytics algorithms for a range of CPUs, GPUs, and other accelerators
Previous Article
Archanan Declares Beta Launch of its Archanan Development Cloud

Archanan launched its Archanan beta program that will make testing by supercomputing centers, enterprises, ...

Next Article
Five Minutes With…Steen Graham, GM, IoT Ecosystem, Intel
Five Minutes With…Steen Graham, GM, IoT Ecosystem, Intel

The IoT is just starting to embrace AI and neural nets. While previously a Cloud-based application, such al...