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Real-time results obtained from an unsupervised feature extraction system using Restricted Boltzmann Machines (RBMs) implemented on FPGA are presented. The feature extraction application is demonstrated using the MNIST dataset, and the weights storing features are visualized in real-time. A digit classification is also performed based on the learning results. Our demonstration system performs 134...
Remarkable hardware robustness of deep learning (DL) is revealed by error injection analyses performed using a custom hardware model implementing parallelized restricted Boltzmann machines (RBMs). RBMs in deep belief networks demonstrate robustness against memory errors during and after learning. Fine-tuning significantly affects the recovery of accuracy for static errors injected to the structural...
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