AI Models Optimized for Edge Applications to Address Resource Constraints
The AI industry is increasingly focusing on optimizing AI models for edge applications, where devices have limited area, power, and memory. This contrasts with the demand for large language models (LLMs) in data centers. Designers are employing various compression techniques, such as quantization, distillation, and pruning, to reduce the size of AI models while maintaining their output capability. The primary challenge is to right-size an AI model for an embedded application by first determining its purpose and the target device's functions. Small language models (SLMs) and compact edge models require tight system-level integration to ensure optimal efficiency, moving away from a 'largest model wins' mentality to 'experience per watt.'
