Name
Abstract | ||
|---|---|---|
DRAM failures are one of the major hardware threats to the reliability of large-scale data centers since the uncorrectable errors in DRAMs may cause servers to shut down. Existing works try to solve this problem by predicting DRAM failures in advance with Machine Learning models. In these works, correctable errors (CEs) are generally deemed as the most important feature. The major reason behind CE... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/VTS50974.2021.9441059 | 2021 IEEE 39th VLSI Test Symposium (VTS) |
Keywords | DocType | ISSN |
Measurement,Data centers,Microscopy,Random access memory,Machine learning,Predictive models,Very large scale integration | Conference | 1093-0167 |
ISBN | Citations | PageRank |
978-1-6654-1949-9 | 0 | 0.34 |
References | Authors | |
0 | 12 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xingyi Wang | 1 | 0 | 0.34 |
| Li Yu | 2 | 90 | 30.48 |
| Yiquan Chen | 3 | 1 | 0.73 |
| Shiwen Wang | 4 | 0 | 0.34 |
| Yin Du | 5 | 0 | 0.34 |
| Cheng He | 6 | 66 | 13.22 |
| YuZhong Zhang | 7 | 0 | 0.34 |
| Pinan Chen | 8 | 0 | 0.34 |
| Xin Li | 9 | 495 | 68.25 |
| Wenjun Song | 10 | 0 | 0.34 |
| Qiang Xu | 11 | 2165 | 135.87 |
| Li Jiang | 12 | 0 | 1.35 |