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Identifying Hardware Design Challenges and AI at the Edge

Identifying Hardware Design Challenges and AI at the Edge

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 The field of artificial intelligence and machine learning - just like any other industry where innovation happens - faces lots of challenges, and specialists are relentlessly looking for ways to overcome them. In this episode, Mike and Ellie tackle some of these challenges and discuss the different compute platforms, their limitations, and the surge of new platform development, as well as the many challenges that hardware designers face as they try to move AI to IoT edge devices. Tune in, and learn some of the challenges of implementing the latest cutting-edge neural network algorithms on today's compute platforms.   In this episode, you will learn: The amount of energy neural networks use. (00:54) Why analog starts to be in the spotlight again. (04:30) How applications moving to the Edge impacts training and inferencing. (05:39) Data movement requires most of the energy consumption. (07:50) Connect with Mike Fingeroff: LinkedIn Connect with Ellie Burns: LinkedIn Resources: Catapult High-Level Synthesis Siemens EDA

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