Roadmap for unconventional computing with nanotechnology

IF 2.5 4区 材料科学 Q3 MATERIALS SCIENCE, MULTIDISCIPLINARY Nano Futures Pub Date : 2024-03-28 DOI:10.1088/2399-1984/ad299a
Giovanni Finocchio, Jean Anne C Incorvia, Joseph S Friedman, Qu Yang, Anna Giordano, Julie Grollier, Hyunsoo Yang, Florin Ciubotaru, Andrii V Chumak, Azad J Naeemi, Sorin D Cotofana, Riccardo Tomasello, Christos Panagopoulos, Mario Carpentieri, Peng Lin, Gang Pan, J Joshua Yang, Aida Todri-Sanial, Gabriele Boschetto, Kremena Makasheva, Vinod K Sangwan, Amit Ranjan Trivedi, Mark C Hersam, Kerem Y Camsari, Peter L McMahon, Supriyo Datta, Belita Koiller, Gabriel H Aguilar, Guilherme P Temporão, Davi R Rodrigues, Satoshi Sunada, Karin Everschor-Sitte, Kosuke Tatsumura, Hayato Goto, Vito Puliafito, Johan Åkerman, Hiroki Takesue, Massimiliano Di Ventra, Yuriy V Pershin, Saibal Mukhopadhyay, Kaushik Roy, I- Ting Wang, Wang Kang, Yao Zhu, Brajesh Kumar Kaushik, Jennifer Hasler, Samiran Ganguly, Avik W Ghosh, William Levy, Vwani Roychowdhury, Supriyo Bandyopadhyay
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Abstract

In the ‘Beyond Moore’s Law’ era, with increasing edge intelligence, domain-specific computing embracing unconventional approaches will become increasingly prevalent. At the same time, adopting a variety of nanotechnologies will offer benefits in energy cost, computational speed, reduced footprint, cyber resilience, and processing power. The time is ripe for a roadmap for unconventional computing with nanotechnologies to guide future research, and this collection aims to fill that need. The authors provide a comprehensive roadmap for neuromorphic computing using electron spins, memristive devices, two-dimensional nanomaterials, nanomagnets, and various dynamical systems. They also address other paradigms such as Ising machines, Bayesian inference engines, probabilistic computing with p-bits, processing in memory, quantum memories and algorithms, computing with skyrmions and spin waves, and brain-inspired computing for incremental learning and problem-solving in severely resource-constrained environments. These approaches have advantages over traditional Boolean computing based on von Neumann architecture. As the computational requirements for artificial intelligence grow 50 times faster than Moore’s Law for electronics, more unconventional approaches to computing and signal processing will appear on the horizon, and this roadmap will help identify future needs and challenges. In a very fertile field, experts in the field aim to present some of the dominant and most promising technologies for unconventional computing that will be around for some time to come. Within a holistic approach, the goal is to provide pathways for solidifying the field and guiding future impactful discoveries.
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利用纳米技术实现非常规计算的路线图
在 "超越摩尔定律 "的时代,随着边缘智能的不断提高,采用非常规方法的特定领域计算将变得越来越普遍。同时,采用各种纳米技术将在能源成本、计算速度、减少占地面积、网络弹性和处理能力等方面带来好处。利用纳米技术制定非常规计算路线图以指导未来研究的时机已经成熟,本论文集旨在满足这一需求。作者利用电子自旋、记忆器件、二维纳米材料、纳米磁体和各种动力系统为神经形态计算提供了全面的路线图。他们还论述了其他范例,如伊辛机、贝叶斯推理引擎、使用 p 位的概率计算、内存中的处理、量子存储器和算法、使用 Skyrmions 和自旋波的计算,以及在资源严重受限的环境中用于增量学习和解决问题的大脑启发计算。与基于冯-诺依曼架构的传统布尔计算相比,这些方法具有优势。随着人工智能计算需求的增长速度是电子技术摩尔定律的 50 倍,地平线上将会出现更多非常规的计算和信号处理方法,本路线图将有助于确定未来的需求和挑战。在这个非常肥沃的领域,该领域的专家旨在介绍未来一段时间内将出现的一些非常规计算的主导技术和最有前途的技术。在整体方法中,目标是为巩固该领域和指导未来有影响力的发现提供途径。
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来源期刊
Nano Futures
Nano Futures Chemistry-General Chemistry
CiteScore
4.30
自引率
0.00%
发文量
35
期刊介绍: Nano Futures mission is to reflect the diverse and multidisciplinary field of nanoscience and nanotechnology that now brings together researchers from across physics, chemistry, biomedicine, materials science, engineering and industry.
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