To further improve the cost models in FusePlanner for better capturing the performance characteristics of different GPU architectures and workloads, the following enhancements can be considered:
Dynamic Cost Modeling: Implement dynamic cost modeling techniques that adapt to the specific characteristics of the GPU architecture and workload at runtime. This can provide more accurate estimations based on real-time data.
Incorporating Hardware Metrics: Integrate hardware performance metrics, such as memory bandwidth, cache sizes, and compute capabilities, into the cost models to better reflect the actual behavior of the GPU during inference.
Machine Learning-based Optimization: Utilize machine learning algorithms to train the cost models on a diverse set of GPU architectures and workloads, enabling them to learn and adapt to complex patterns and variations.
Fine-grained Analysis: Enhance the granularity of the cost models to capture detailed interactions between different layers, memory accesses, and compute operations, providing a more comprehensive view of the performance characteristics.
Validation and Calibration: Regularly validate and calibrate the cost models against real-world performance data to ensure their accuracy and reliability in predicting the behavior of fused convolutions on GPUs.
By incorporating these enhancements, FusePlanner can offer more precise and tailored recommendations for optimizing fusion strategies and achieving efficient inference on a wide range of GPU architectures and neural network workloads.