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"Unleashing Generative AI's Next Wave at the Edge"

Available On-Demand | Brought to you by Ambarella and Omdia

Generative AI has captured widespread interest by offering businesses and consumers unprecedented opportunities to directly utilize AI in ways that were previously science fiction. Yet, the very expansion of computing power and AI capabilities that has enabled these advancements is now becoming a challenge, as AI training and inference emerge as the dominant computing workloads of the 2020s. In particular, the hardware currently being used to put GenAI at our collective fingertips presents a significant obstacle to extending the capabilities of emerging AI technologies to edge computing, which could unlock considerable value.

The progress in Generative AI so far has primarily focused on the training of ever-growing language models on servers usually located in datacenters. This current focus is just the beginning, setting the stage for broader technology adoption through scalable deployment at the edge. There is a host of additional use cases that are poised to create a much larger wave of embedded applications for GenAI, from robotics and consumer electronics to security and autonomous driving. However, achieving integration at this scale brings technological hurdles such as energy efficiency, on-device fine tuning, reliability and cost into sharp focus, all of which demand tailored system-on-chip (SoC) designs.

In a world of increasing choice and powerful models, what is the right approach, and what should be the focus between performance, power and price when it comes to hardware?

Join this webinar with Omdia and Ambarella to learn:

• Why new AI inference processing architectures are required to enable GenAI at the edge, and how GenAI processing will be distributed throughout the computing hierarchy (from datacenter to edge devices)
• Why the industry will trend towards smaller, more specific models – especially for visual analytics
• How applications such as Robotics and Smart Cities can benefit from this approach
• How developers can get started on the right adoption path for Generative AI 

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