Name
Abstract | ||
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Racetrack Memory(RM) has attracted abundant attention of memory researchers recently. RM can achieve ultra-high storage density, fast access velocity and non-volatility. Former research has demonstrated that RM has potential to serve as on-chip cache or main memory. However, RM has more flexibility and difficulty in design space of main memory because it has more device level design parameters. The layout of macro unit (MU) needs trade-off among area, access performance and energy consumption, and its shift operation introduces extra dimension of design space. In this paper, we explore these design parameters and analyze their relationship in memory design space in both device and system levels. Based on the results, we also propose a hybrid MU structure to further optimize read intensive applications. Experimental results demonstrated the existence of regularity between design parameters and performance features. The optimized layout of racetrack MU is suggested for application areas such as big-data and IoT which need cost-effective and energy-efficient memory respectively. Together with hybrid MU structures, RM can be designed with more flexibility so that specific structures are suitable for specific applications which make "All stack optimization" possible in memory structure level.
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Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ASPDAC.2018.8297351 | ASP-DAC |
Keywords | Field | DocType |
performance analysis,ultrahigh storage density,fast access velocity,on-chip cache,main memory,device level design parameters,memory design space,hybrid MU structure,optimized layout,racetrack MU,cost-effective energy-efficient memory,memory structure level,Racetrack Memory | Dram,Computer architecture,System on a chip,Computer science,Cache,Electronic engineering,Non-volatile memory,Macro,Energy consumption,Racetrack memory,Built-in self-test | Conference |
ISSN | ISBN | Citations |
2153-6961 | 978-1-4503-6007-4 | 1 |
PageRank | References | Authors |
0.35 | 16 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hongbin Zhang | 1 | 2 | 1.37 |
| Chao Zhang | 2 | 423 | 38.17 |
| Qingda Hu | 3 | 22 | 3.78 |
| Chengmo Yang | 4 | 302 | 32.31 |
| Jiwu Shu | 5 | 709 | 72.71 |