One of the promise of IoT is to allow bringing the intelligence of the Cloud to the Edge to run IoT data analytics as close as possible to the data source. This allows to reduce latencies, optimize performance and response times, support offline scenario, comply with privacy policies and regulations, reduce data transfer cost, and more…
One thing you really have to consider when bringing Artificial Intelligence to the edge is the hardware you will need to run these powerful algorithms. Ted Way from the Azure Machine Learning team joins Olivier on the IoT Show to discuss hardware acceleration at the Edge for AI. We will discuss scenarios and technologies Microsoft develops and uses to accelerate AI in the Cloud and at the Edge such as Graphic cards, FPGA, CPU,… To illustrate all this, Ted walks us through real life scenarios and demos IoT Edge running Machine Learning vision algorithms.
Learn more about hardware acceleration for AI at the Edge: https://docs.microsoft.com/azure/machine-learning/service/concept-accelerate-with-fpgas
Create a Free Account (Azure): https://aka.ms/aft-iot
Hardware Acceleration for AI at the Edge | Internet of Things Show
Source: MSDN Channel 9