Micromagnetic simulations for spin-based neuromorphic computing
Although modern A.I has enabled many new innovations such as voice assistant and facial recognitions, current A.I computers are still a long way from achieving human-like flexibility in problem solving and the ability to learn from unstructured stimuli with energy efficiency comparable to a human br...
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| Format: | Final Year Project |
| Language: | English |
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Nanyang Technological University
2022
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| Online Access: | https://hdl.handle.net/10356/156927 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | Although modern A.I has enabled many new innovations such as voice assistant and facial recognitions, current A.I computers are still a long way from achieving human-like flexibility in problem solving and the ability to learn from unstructured stimuli with energy efficiency comparable to a human brain.
Traditional von Neumann computers are highly inefficient at solving unstructured problems and as such, neuromorphic computers which mimic the biological neural network of a human brain have been researched extensively in a bid to create more powerful artificial neuromorphic computing systems that can compete with the human brain in unstructured problem-solving efficiency.
Spin-based neuromorphic computing is a promising candidate that has the potential to create high-performance and low powered neuromorphic network. In our model, we make use of domain wall switching to model the Leaky-Integrate-and-Fire neurons. To further explore domain wall based neuromorphic computing, we ran simulations of our spin-based design to observe its Leaky-Integrate functionalities and analysed it suitability for use in neuromorphic computing. |
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