Academic Journal
Roadmap on emerging hardware and technology for machine learning
| Τίτλος: | Roadmap on emerging hardware and technology for machine learning |
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| Συγγραφείς: | Berggren, K, Xia, Q, Likharev, KK, Strukov, DB, Jiang, H, Mikolajick, T, Querlioz, D, Salinga, M, Erickson, JR, Pi, S, Xiong, F, Lin, P, Li, C, Chen, Y, Xiong, S, Hoskins, BD, Daniels, MW, Madhavan, A, Liddle, JA, McClelland, JJ, Yang, Y, Rupp, J, Nonnenmann, SS, Cheng, KT, Gong, N, Lastras-Montaño, MA, Talin, AA, Salleo, A, Shastri, BJ, de Lima, TF, Prucnal, P, Tait, AN, Shen, Y, Meng, H, Roques-Carmes, C, Cheng, Z, Bhaskaran, H, Jariwala, D, Wang, H, Shainline, JM, Segall, K, Yang, JJ, Roy, K, Datta, S, Raychowdhury, A |
| Στοιχεία εκδότη: | //www.iop.org/journals/nano United Kingdom |
| Έτος έκδοσης: | 2020 |
| Συλλογή: | University of Hong Kong: HKU Scholars Hub |
| Θεματικοί όροι: | artificial intelligence, machine learning, neural network models, neuromorphic computing, hardware technologies |
| Περιγραφή: | 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. ; published_or_final_version |
| Τύπος εγγράφου: | article in journal/newspaper |
| Γλώσσα: | English |
| Relation: | Nanotechnology; Nanotechnology, 2020, v. 32 n. 1, p. article no. 012002; article no. 012002; 320857; WOS:000579646000001; https://hub.hku.hk/handle/10722/295356; 32 |
| DOI: | 10.1088/1361-6528/aba70f |
| Διαθεσιμότητα: | https://hub.hku.hk/handle/10722/295356 https://doi.org/10.1088/1361-6528/aba70f |
| Rights: | Nanotechnology. Copyright © Institute of Physics Publishing. ; This is an author-created, un-copyedited version of an article published in [insert name of journal]. IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record is available online at http://dx.doi.org/[insert DOI]. ; This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. |
| Αριθμός Καταχώρησης: | edsbas.6FB6110A |
| Βάση Δεδομένων: | BASE |
| DOI: | 10.1088/1361-6528/aba70f |
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