Data-centric computation mode for convolution in deep neural networks

Deep Convolutional Neural Network (CNN) based methods have shown outstanding performance in a wide range of applications. Nowadays neural networks become deeper, leading to demand of substantial computation and memory resources. Customized hardware is one option which maintains high performance in lower energy consume than general CPUs or GPUs. While hardware designing, we need to address the problem of massive data transmission, and ensure high throughput at the same time. Actually, substantial data transfer consumes more energy than computation. The larger scales of neural networks become, the harder to solve this problem. In this paper, we focus on convolution operation, which occupies nearly 90% computation and runtime in deep CNN. We propose a data-centric computation mode for convolution, which declines the total requirements of data transfer during convolution processing period efficiently, and utilizes data locality to achieve high throughput. Different from previous methods, which adopt efficient on-chip memory hierarchy or focus on partial results' movements, our proposed method concentrates on operands themselves in convolution, minimizing data transfer right from the start. Obviously, it can be combined with others to achieve higher energy efficient. Furthermore, we also simulate and analyse the hardware overhead of our data-centric convolution, corroborating its potentiality of performing high throughput in low energy consumption.