Lightning Talk: Bridging Neuro-Dynamics and Cognition
Brain-inspired computing models have shown great potential to outperform today's deep learning solutions in terms of robustness and energy efficiency. Particularly, Spiking Neural Networks (SNNs) and HyperDimensional Computing (HDC) have shown promising results in enabling efficient and robust...
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| Published in | 2023 60th ACM/IEEE Design Automation Conference (DAC) pp. 1 - 2 |
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| Main Author | |
| Format | Conference Proceeding |
| Language | English |
| Published |
IEEE
09.07.2023
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| Subjects | |
| Online Access | Get full text |
| DOI | 10.1109/DAC56929.2023.10247931 |
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| Summary: | Brain-inspired computing models have shown great potential to outperform today's deep learning solutions in terms of robustness and energy efficiency. Particularly, Spiking Neural Networks (SNNs) and HyperDimensional Computing (HDC) have shown promising results in enabling efficient and robust cognitive learning. Despite the success, these two brain-inspired models have different strengths. While SNN mimics the physical properties of the human brain, HDC models the brain on a more abstract and functional level. Their design philosophies demonstrate complementary patterns that motivate their combination. With the help of the classical psychological model of memory, we aim to explore the difference between Spiking neural networks and hyperdimensional computing and how they can combine to develop a more advanced cognitive learning model. |
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| DOI: | 10.1109/DAC56929.2023.10247931 |