Name
Abstract | ||
|---|---|---|
Recently Spin Transfer Torque Random Access Memory (STT-MRAM) technology has drawn a lot of attention for the direct implementation of neural networks, because it offers several advantages such as near-zero leakage, high endurance, good scalability, small foot print and CMOS compatibility. The storing device in this technology, the Magnetic Tunnel Junction (MTJ), is developed using magnetic layers that requires new fabrication materials and processes. Due to complexities of fabrication steps and materials, MTJ cells are subject to various failure mechanisms. As a consequence, the functionality of the neuromorphic computing architecture based on this technology is severely affected. In this paper, we have developed a framework to analyze the functional capability of the neural network inference in the presence of the several MTJ defects. Using this framework, we have demonstrated the required memory array size that is necessary to tolerate the given amount of defects and how to actively decrease this overhead by disabling parts of the network.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3287624.3288745 | ASP-DAC |
Keywords | Field | DocType |
MTJ, defect modeling, neuromorphic computing | Leakage (electronics),Computer science,Spintronics,Neuromorphic engineering,Electronic engineering,Tunnel magnetoresistance,Spin-transfer torque,Artificial neural network,Scalability,Random access | Conference |
Citations | PageRank | References |
2 | 0.39 | 25 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Christopher Münch | 1 | 3 | 1.43 |
| Rajendra Bishnoi | 2 | 132 | 19.64 |
| Mehdi B. Tahoori | 3 | 1537 | 163.44 |