A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf
.
An energy-efficient coarse grained spatial architecture for convolutional neural networks AlexNet
2017
IEICE Electronics Express
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
doi:10.1587/elex.14.20170595
fatcat:mujgvpftarhaxnmmwrkfhbf2we