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

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.