The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
Roadmap on emerging hardware and technology for machine learning
Open accessAbstract
Recent progress in artificial intelligence is largely attributed to the rapid development of machine learning, especially in the algorithm and neural network models. However, it is the performance of the hardware, in particular the energy efficiency of a computing system that sets the fundamental limit of the capability of machine learning. Data-centric computing requires a revolution in hardware systems, since traditional digital computers based on transistors and the von Neumann architecture were not purposely designed for neuromorphic computing. A hardware platform based on emerging devices and new architecture is the hope for future computing with dramatically improved throughput and energy efficiency. Building such a system, nevertheless, faces a number of challenges, ranging from materials selection, device optimization, circuit fabrication and system integration, to name a few. The aim of this Roadmap is to present a snapshot of emerging hardware technologies that are potentially beneficial for machine learning, providing the Nanotechnology readers with a perspective of challenges and opportunities in this burgeoning field.
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Citations
Cites
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“Depending on the properties of the device, such hardware is suitable for three types of applications [2].”
BackgroundMethodInfluentialSelf-citationMemristors with diffusive dynamics as synaptic emulators for neuromorphic computing
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Cited by
Photonics for artificial intelligence and neuromorphic computing
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Self-citationNeuro-inspired electronic skin for robots
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Integrated Electro-Optics on Thin-Film Lithium Niobate
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Metadata
- Authors
- Berggren, K., Xia, Q., Likharev, K. K., Strukov, D. B., Jiang, H., Mikolajick, T., Querlioz, D., Salinga, M., Erickson, J. R., Pi, S., Xiong, F., Lin, P., Li, C., Chen, Y., Xiong, S., Hoskins, B. D., Daniels, M. W., Madhavan, A., Liddle, J. A., McClelland, J. J., Yang, Y., Rupp, J., Nonnenmann, S. S., Cheng, K.-T., Gong, N., Lastras-Montaño, M. A., Talin, A. A., Salleo, A., Shastri, B. J., de Lima, T. F., Prucnal, P., Tait, A. N., Shen, Y., Meng, H., Roques-Carmes, C., Cheng, Z., Bhaskaran, H., Jariwala, D., Wang, H., Shainline, J. M., Segall, K., Yang, J. J., Roy, K., Datta, S., & Raychowdhury, A.
- Venue
- Nanotechnology
- Publisher
- IOP Publishing
- Published
- 2020-10-20
- Volume
- 32
- Issue
- 1
- Pages
- 012002
- ISSN
- 0957-4484 (ISSN-L)
- 1361-6528
- OpenAlex
- W3043159233
- Cited by
- 190
- References
- 20
- FWCI
- 10.80
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Berggren, K., Xia, Q., Likharev, K. K., Strukov, D. B., Jiang, H., Mikolajick, T., Querlioz, D., Salinga, M., Erickson, J. R., Pi, S., Xiong, F., Lin, P., Li, C., Chen, Y., Xiong, S., Hoskins, B. D., Daniels, M. W., Madhavan, A., Liddle, J. A., . . . Raychowdhury, A. (2021). Roadmap on emerging hardware and technology for machine learning. Nanotechnology. https://doi.org/10.1088/1361-6528/aba70f
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