Roadmap for Unconventional Computing with Nanotechnology
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.
Cite
@article{arxiv.2301.06727,
title = {Roadmap for Unconventional Computing with Nanotechnology},
author = {Giovanni Finocchio and Jean Anne C. Incorvia and Joseph S. Friedman and Qu Yang and Anna Giordano and Julie Grollier and Hyunsoo Yang and Florin Ciubotaru and Andrii Chumak and Azad J. Naeemi and Sorin D. Cotofana and Riccardo Tomasello and Christos Panagopoulos and Mario Carpentieri and Peng Lin and Gang Pan and J. Joshua Yang and Aida Todri-Sanial and Gabriele Boschetto and Kremena Makasheva and Vinod K. Sangwan and Amit Ranjan Trivedi and Mark C. Hersam and Kerem Y. Camsari and Peter L. McMahon and Supriyo Datta and Belita Koiller and Gabriel H. Aguilar and Guilherme P. Temporão and Davi R. Rodrigues and Satoshi Sunada and Karin Everschor-Sitte and Kosuke Tatsumura and Hayato Goto and Vito Puliafito and Johan Åkerman and Hiroki Takesue and Massimiliano Di Ventra and Yuriy V. Pershin and Saibal Mukhopadhyay and Kaushik Roy and I-Ting Wang and Wang Kang and Yao Zhu and Brajesh Kumar Kaushik and Jennifer Hasler and Samiran Ganguly and Avik W. Ghosh and William Levy and Vwani Roychowdhury and Supriyo Bandyopadhyay},
journal= {arXiv preprint arXiv:2301.06727},
year = {2024}
}
Comments
80 pages accepted in Nano Futures