Robustness of hardware-oriented restricted Boltzmann machines in deep belief networks for reliable processing

Kodai Ueyoshi, Takao Marukame, Tetsuya Asai, Masato Motomura, Alexandre Schmid
2016 Nonlinear Theory and Its Applications IEICE  
Remarkable hardware robustness of deep learning is revealed from an error-injection analysis performed using a custom hardware model implementing parallelized restricted Boltzmann machines (RBMs). RBMs used in deep belief networks (DBNs) demonstrate robustness against memory errors during and after learning. Fine-tuning has a significant impact on the recovery of accuracy under the presence of static errors that may modify structural data of RBMs. The proposed hardware networks with fine-graded
more » ... ks with fine-graded memory distribution are observed to tolerate memory errors, thereby resulting in a reliable deep learning hardware platform, potentially suitable to safety-critical embedded applications.
doi:10.1587/nolta.7.395 fatcat:hde3rpci2fbdlpczgahdrh7fze