Research on a 2D Weight Stationary Dataflow Architecture

With the development of artificial intelligence algorithms, convolutional neural networks (CNN) is more and more widely used in image, audio and other aspects, and the amount of calculation of CNN algorithms is also increasing. Weight stationary (WS) is a dataflow that maximizes the use of convolutional reuse and filter reuse by fixing weights in registers. However, the current WS dataflow structure has the problem that the pipeline filling time is lager. This paper studies a 2D-WS dataflow structure that uses PE adders to remove the FIFO between PE lines. This kind of 2D-WS dataflow structure reduces the pipeline filling time by nearly 2.7 times when calculating AlexNet, and can flexibly adjust the stride size.