Edge Devices and LLMs: What's Ahead for AI
OnTrack: The PCB Design Podcast
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Balancing Performance and Cost in Designing Edge Devices for AI Applications
The chapter delves into the challenges and importance of optimizing RAM compute for AI models in neural accelerators, especially for edge devices. It explores strategies like reducing parameters to enable large AI models to run efficiently on such devices, allowing for tasks like conversational AI without reliance on the cloud. The conversation also covers the use of hyper-specific models and memory constraints in hardware architecture, showcasing the process of distilling knowledge from large models into smaller, specialized ones for edge computing.
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