Tensor Virtualization Technique to Support Efficient Data Reorganization for CNN Accelerators

Abstract

There is a growing need for data reorganization in recent neural networks for various applications such as Generative Adversarial Networks(GANs) that use transposed convolution and U-Net that requires upsampling. We propose a novel technique, called tensor virtualization technique, to perform data reorganization efficiently with a minimal hardware addition for adder-tree based CNN accelerators. In the proposed technique, a data reorganization request is specified with a few parameters and data reorganization is performed in the virtual space without overhead in the physical memory. It allows existing adder-tree-based CNN accelerators to accelerate a wide range of neural networks that require data reorganization, including U-Net, DCGAN, and SRGAN.