Optimizing Depthwise Separable Convolution Operations on GPUs

Abstract

The depthwise separable convolution is widely used to reduce the computation overhead of multi-channel 2D convolutions. Existing implementations of depthwise separable convolutions target accelerating model training with large batch size with a large number of samples to be processed at once. Such approaches are inadequate for small-batch-sized model training and the typical scenario of model inference where the model takes in a few samples at once. This paper aims to bridge the gap of optimizing depthwise separable convolutions by targeting the GPU architecture. We achieve this by designing two novel algorithms to improve the column and row reuse of convolution operations to reduce the number of memory operations. Our approach employs a dynamic tile size scheme to adaptively distribute the computational data across GPU threads to improve the GPU utilization and to hide the memory access latency. We apply our approach on two GPU platforms: NVIDIA RTX 2080Ti and NVIDIA Jetson AGX Xavier GPUs, and two data types: 32-bit floating point (FP32) and 8-bit integer (INT8). We compared our approach against cuDNN that is heavily tuned for the NVIDIA GPU architecture. Experimental results show that our approach delivers over 2 (up to 3) performance improvement over cuDNN