The proposed techniques for fusing depthwise and pointwise convolutions can be extended to handle other types of convolutions, such as standard convolutions, by adapting the fusion approach to accommodate the specific characteristics of these convolutions. Standard convolutions involve a full cross-correlation operation between the input and the filter, unlike the separable nature of depthwise and pointwise convolutions.
To extend the fusion approach to standard convolutions, the key considerations would include:
Dataflow Optimization: Standard convolutions require a different dataflow compared to depthwise and pointwise convolutions. The fusion technique would need to optimize the dataflow to efficiently process the input and filter data for standard convolutions.
Memory Access Patterns: Standard convolutions involve larger filter sizes and more complex computations, leading to different memory access patterns. The fusion approach would need to account for these patterns to minimize memory accesses and improve performance.
Redundant Computations: Standard convolutions may introduce more redundant computations when fused with other layers. Strategies to minimize redundant computations while maintaining efficiency would be crucial.
By adapting the fusion techniques to handle standard convolutions, the overall efficiency and performance of neural network models can be further enhanced, leading to more optimized inference on GPUs.