AI Bibliography
AI Bibliography |
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Markovi'c, D., Mizrahi, A., Querlioz, D., & Grollier, J. (2020). Physics for neuromorphic computing. Nature Reviews Physics, 2(9), 499–510. |
| Resource type: Journal Article BibTeX citation key: Markovic2020 View all bibliographic details  |
Categories: Artificial Intelligence, Biological Science, Cognitive Science, Computer Science, Engineering, General, Medical science, Military Science, Neuroscience Subcategories: Augmented cognition, Machine intelligence, Machine learning, Military research, Neural nets, Neurosymbolic, Synthetic intelligence Creators: Grollier, Markovi'c, Mizrahi, Querlioz Publisher: Collection: Nature Reviews Physics |
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Neuromorphic computing takes inspiration from the brain to create energy-efficient hardware for information processing, capable of highly sophisticated tasks. Systems built with standard electronics achieve gains in speed and energy by mimicking the distributed topology of the brain. Scaling-up such systems and improving their energy usage, speed and performance by several orders of magnitude requires a revolution in hardware. We discuss how including more physics in the algorithms and nanoscale materials used for data processing could have a major impact in the field of neuromorphic computing. We review striking results that leverage physics to enhance the computing capabilities of artificial neural networks, using resistive switching materials, photonics, spintronics and other technologies. We discuss the paths that could lead these approaches to maturity, towards low-power, miniaturized chips that could infer and learn in real time.
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