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| Content Provider | IET Digital Library |
|---|---|
| Author | Williams, L. |
| Abstract | The chances are high that most people you know have never heard of skyrmions. This is natural, because they are rather murky products of quantum field theory that act like particles without actually being particles. So far so obscure. Skyrmions, however, may soon be as well known as silicon chips. That's because they have the potential to change the face of computing, revolutionising the fields of data storage, information processing and artificial intelligence. Skyrmions are quasi-particles - little twists in energy fields that look and behave like particles. They can be moved around, interact with other skyrmions and be created and destroyed. Like particles, they can be extremely stable, lasting years in the right conditions. Based on their inherent stability, the most promising application for skyrmions is in data storage, where each skyrmion acts as a bit. However, there are challenges to overcome - researchers have only been able to find magnetic skyrmions that are too slow and too large to compete with existing memory devices. Until now. In December 2018, a team from MIT published a paper showing that they could achieve the right sizes and, to some degree, speeds to be competitive.The size, speed and stability of skyrmions could mean smaller and faster memory devices are around the corner. What's more, because of skyrmions' inherent stability, they are still there when you turn the power off. This means that something called normally-off computing could be achieved. Skyrmions' potential extends even beyond classical computing. Neuromorphic computing seeks to use neural networks to mimic the way the brain processes information and is behind some of the most powerful machine-learning algorithms.structure. |
| Starting Page | 66 |
| Ending Page | 69 |
| Page Count | 4 |
| ISSN | 17509637 |
| Volume Number | 14 |
| e-ISSN | 17509645 |
| Issue Number | Issue 5, Jun (2019) |
| Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/et/14/5 |
| Journal | Engineering & Technology |
| Access Restriction | Limited |
| Subject Keyword | Artificial Intelligence Circuit Implementations Data Storage Information Processing Machine-learning Algorithm Magnetic Skyrmions Memory Architecture Memory Circuit Memory Device Neural Chips Neural Net Device Neural Nets Neural Network Neuromorphic Computing Normally-off Computing Power Aware Computing Quantum Computation Quantum Computing Technique Quantum Field Theory Quasi-Particle Silicon Chips Storage Requirements |
| Content Type | Text |
| Resource Type | Article |
| Subject | Electrical and Electronic Engineering |
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