Name
Abstract | ||
|---|---|---|
The emergence of non-volatile memories (NVM) such as resistive-oxide random access memory (RRAM), magnetoresistive random access memory (MRAM), and phase change memory (PCM) enables brain-inspired neuromorphic computing. However, due to immature fabrication process, NVMs are prone to process variations and manufacturing defects, which must be investigated for effective defect-to-fault mapping, high-coverage test generation, and diagnostics-driven yield learning. In this paper, we present a survey of research on fault modeling, test generation methodologies, and fault-tolerant design of neuromorphic computing systems based on RRAM and MRAM. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ITC44170.2019.9000146 | 2019 IEEE International Test Conference (ITC) |
Keywords | Field | DocType |
RRAM,MRAM,fault-tolerant neuromorphic computing systems,nonvolatile memories,NVM,resistive-oxide random access memory,magnetoresistive random access memory,phase change memory,brain-inspired neuromorphic computing,immature fabrication process,manufacturing defects,defect-to-fault mapping,high-coverage test generation,diagnostics-driven yield learning,fault modeling,test generation methodologies,fault-tolerant design | Phase-change memory,Computer architecture,Computer science,Fault modeling,Neuromorphic engineering,Electronic engineering,Magnetoresistive random-access memory,Fault tolerance,Random access memory,Resistive random-access memory | Conference |
ISSN | ISBN | Citations |
1089-3539 | 978-1-7281-4824-3 | 0 |
PageRank | References | Authors |
0.34 | 27 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Arjun Chaudhuri | 1 | 17 | 7.07 |
| Mengyun Liu | 2 | 34 | 8.19 |
| K Chakrabarty | 3 | 8173 | 636.14 |