Name
Title | ||
|---|---|---|
Memory-error tolerance of scalable and highly parallel architecture for restricted Boltzmann machines in Deep Belief Network |
Abstract | ||
|---|---|---|
A key aspect of constructing highly scalable Deep-learning microelectronic systems is to implement fault tolerance in the learning sequence. Error-injection analyses for memory is performed using a custom hardware model implementing parallelized restricted Boltzmann machines (RBMs). It is confirmed that the RBMs in Deep Belief Networks (DBNs) provides remarkable robustness against memory errors. Fine-tuning has significant effects on recovery of accuracy for static errors injected to the structural data of RBMs during and after learning, which are either at cell-level or block level. The memory-error tolerance is observable using our hardware networks with fine-graded memory distribution. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/ISCAS.2016.7527244 | 2016 IEEE International Symposium on Circuits and Systems (ISCAS) |
Keywords | Field | DocType |
Deep Learning,restricted Boltzmann machines (RBMs),fault tolerance | Data modeling,Boltzmann machine,Computer science,Parallel computing,Deep belief network,Robustness (computer science),Fault tolerance,Artificial intelligence,Deep learning,Memory errors,Scalability | Conference |
ISSN | ISBN | Citations |
0271-4302 | 978-1-4799-5342-4 | 2 |
PageRank | References | Authors |
0.63 | 5 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kodai Ueyoshi | 1 | 3 | 1.65 |
| Takao Marukame | 2 | 4 | 2.69 |
| Tetsuya Asai | 3 | 121 | 26.53 |
| Masato Motomura | 4 | 91 | 27.81 |
| Alexandre Schmid | 5 | 29 | 11.91 |