Applying mixed-precision quantization and hardware-aware mapping optimization to real-world, large-scale CNN models may face several challenges:
Computational Complexity: Large-scale CNN models have complex architectures with numerous layers, making the optimization process computationally intensive. Handling the increased computational load efficiently while maintaining optimization quality is a challenge.
Memory Constraints: Large-scale models require significant memory resources, and optimizing memory utilization through quantization and mapping without compromising performance can be challenging. Balancing memory constraints with computational requirements is crucial.
Scalability: Ensuring that the optimization framework scales effectively to handle the complexity and size of large-scale CNN models is essential. Scaling the framework to accommodate the increased model complexity while maintaining optimization efficiency poses a challenge.
Generalization: Adapting the framework to generalize well across different types of large-scale CNN models with varying architectures and requirements is a challenge. Ensuring that the optimization strategies are robust and effective across diverse models is crucial.
Addressing these challenges requires careful algorithm design, efficient resource utilization, and thorough testing on a variety of large-scale CNN models to validate the effectiveness and scalability of the proposed optimization framework.