An energy-efficient coarse grained spatial architecture for convolutional neural networks AlexNet

Abstract

In this paper, we propose a CGSA (Coarse Grained Spatial Architecture) which processes different kinds of convolution with high performance and low energy consumption. The architecture's 16 coarse grained parallel processing units achieve a peak 152 GOPS running at 500 MHz by exploiting local data reuse of image data, feature map data and filter weights. It achieves 99 frames/s on the convolutional layers of the AlexNet benchmark, consuming 264 mW working at 500 MHz and 1 V. We evaluated the architecture by comparing some recent CNN's accelerators. The evaluation result shows that the proposed architecture achieves 3× energy efficiency and 3.5× area efficiency than existing work of the similar architecture and technology proposed by Chen.

Bit-serial systolic accelerator design for convolution operations in convolutional neural networks

“…Thereinto, the Convolutional Neural Network (CNN) has been one of the most representative algorithms due to the excellent learning performance [6]. However, the CNN performance relies on the deep, wide and complex network with huge computation complexity and storage requirements, which limits the applications in mobile or embedded devices [7,8,9]. Therefore, it is of great significance to study the CNN accelerator with high hardware efficiency.…”