Capsule Network Implementation On FPGA
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Artificial intelligent, Capsule neural network, Deep learning, FPGA, Image recognition
Abstract
A capsule neural network (CapsNet) is a new approach in artificial neural network (ANN) that produces a better hierarchical relationship. The performance of CapsNet on graphics processing unit (GPU) is considerably better than convolutional neural network (CNN) at recognizing highly overlapping digits in images. Nevertheless, this new method has not been designed as an accelerator on field programmable gate array (FPGA) to measure the speedup performance and compare it with the GPU. This paper aims to design the CapsNet module (accelerator) on FPGA. The performance between FPGA and GPU will be compared, mainly in terms of speedup and accuracy. The results show that training time on GPU using MATLAB is 789.091 s. Model evaluation accuracy is 99.79% and the validation accuracy is 98.53%. The time required to finish one routing algorithm iteration in MATLAB is 0.043622 s and in FPGA it takes 0.00065s which means FPGA module is 67 times faster than GPU.
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