Applying the fusion approach to more complex neural network architectures beyond CNNs and ViTs presents several potential challenges and trade-offs:
Increased Complexity: Complex architectures may have diverse layer types and dependencies, making it challenging to identify optimal fusion opportunities. The trade-off lies in balancing the complexity of the architecture with the benefits of fusion.
Inter-Layer Dependencies: Complex architectures often have intricate dependencies between layers, requiring careful consideration when fusing to avoid introducing errors or inefficiencies. Balancing these dependencies while optimizing for performance is crucial.
Resource Utilization: Fusion may lead to increased resource utilization, especially in architectures with diverse layer types. Managing resources efficiently and ensuring optimal utilization becomes a critical challenge.
Performance Trade-offs: Fusion may not always result in performance improvements for all layers in complex architectures. Trade-offs between memory access reduction, compute efficiency, and overall inference speed need to be carefully evaluated.
Addressing these challenges involves developing advanced fusion strategies, optimizing cost models for diverse architectures, and considering the specific characteristics of each layer type within the architecture.