Kernel-Segregated Transpose Convolution Operation

TL;DR

This paper introduces a kernel segregation technique to optimize transpose convolution, significantly reducing computation time and resource usage in deep learning applications without extra hardware.

Contribution

The paper presents a novel kernel segregation algorithm that accelerates transpose convolution by reducing unnecessary computations and memory use, applicable across hardware platforms.

Findings

3.09x faster on GPU with flower dataset

2.2x faster training on CPU with MNIST

Reduces memory and computation in transpose convolution

Abstract

Transpose convolution has shown prominence in many deep learning applications. However, transpose convolution layers are computationally intensive due to the increased feature map size due to adding zeros after each element in each row and column. Thus, convolution operation on the expanded input feature map leads to poor utilization of hardware resources. The main reason for unnecessary multiplication operations is zeros at predefined positions in the input feature map. We propose an algorithmic-level optimization technique for the effective transpose convolution implementation to solve these problems. Based on kernel activations, we segregated the original kernel into four sub-kernels. This scheme could reduce memory requirements and unnecessary multiplications. Our proposed method was $3.09 (3.02) \times$ faster computation using the Titan X GPU (Intel Dual Core CPU) with a flower dataset from the Kaggle website. Furthermore, the proposed optimization method can be generalized to existing devices without additional hardware requirements. A simple deep learning model containing one transpose convolution layer was used to evaluate the optimization method. It showed $2.2 \times$ faster training using the MNIST dataset with an Intel Dual-core CPU than the conventional implementation.