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Article Dans Une Revue Nano Futures Année : 2024

Roadmap for unconventional computing with nanotechnology

Giovanni Finocchio
Jean Anne C Incorvia
Joseph S Friedman
Anna Giordano
  • Fonction : Auteur
  • PersonId : 1370437
Andrii V Chumak
Azad J Naeemi
Sorin D Cotofana
Peng Lin
Gang Pan
Joshua Joshua Yang
Vinod K Sangwan
  • Fonction : Auteur
Amit Ranjan Trivedi
  • Fonction : Auteur
Mark C Hersam
Kerem Y Camsari
Peter L Mcmahon
Supriyo Datta
  • Fonction : Auteur
  • PersonId : 1370449
Gabriel H Aguilar
Guilherme P Temporão
Davi R Rodrigues
Karin Everschor-Sitte
Kosuke Tatsumura
Hayato Goto
Vito Puliafito
Johan Åkerman
Hiroki Takesue
Massimiliano Di Ventra
Yuriy V Pershin
Saibal Mukhopadhyay
  • Fonction : Auteur
Kaushik Roy
  • Fonction : Auteur
I- Ting Wang
Wang Kang
Yao Zhu
  • Fonction : Auteur
Brajesh Kumar Kaushik
Jennifer Hasler
Samiran Ganguly
Avik W Ghosh
  • Fonction : Auteur
William Levy
  • Fonction : Auteur
Vwani Roychowdhury
Supriyo Bandyopadhyay

Résumé

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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Dates et versions

hal-04530101 , version 1 (03-04-2024)

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Giovanni Finocchio, Jean Anne C Incorvia, Joseph S Friedman, Qu Yang, Anna Giordano, et al.. Roadmap for unconventional computing with nanotechnology. Nano Futures, 2024, 8 (1), pp.012001. ⟨10.1088/2399-1984/ad299a⟩. ⟨hal-04530101⟩
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