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In recent years, deep learning has attracted lots of research interests for pattern recognition and artificial intelligence. PCA Network (PCANet) is a simple deep learning network with highly competitive performance for texture classification and object recognition. When compared to other deep neural networks such as convolutional neural network (CNN), PCANet has much simpler structure, which makes it attractive for hardware design on an FPGA. In this paper, an efficient, highthroughput, pipeline architecture is proposed for the PCANet classifier. The implementation on an FPGA is more than 1,000 times faster than software execution on a general purpose processor. When evaluated using the MNIST handwritten digits dataset, the PCANet design results an accuracy of about 99.46%.

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