One of the promises of IoT is bringing the intelligence of the Cloud to the Edge, running data analytics as close as possible to the data source. This reduces latencies, optimizes performance and response times, supports offline analytics, compliance with privacy policies and regulations, reduces data transfer costs, and more...
One thing you rmust consider when bringing Artificial Intelligence (AI) to the Edge is the hardware necessary to run these powerful algorithms. Ted Way, from the Azure Machine Learning team, joins Olivier Bloch on The IoT Show to discuss Hardware Acceleration for AI at the Edge. We discuss scenarios and technologies Microsoft develops and uses to accelerate AI in the Cloud and at the Edge, including GPUs, FPGA, CPU, etc. To illustrate all of this, Ted walks us through some real life scenarios and demonstrates IoT Edge running Machine Learning vision algorithms.
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