Roadmap for unconventional computing with nanotechnology

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

doi:10.1088/2399-1984/ad299a

Roadmap for unconventional computing with nanotechnology

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 MakashevaVinod 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^*

^*Corresponding author for this work

Materials Science and Engineering

Research output: Contribution to journal › Review article › peer-review

27 Scopus citations

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.

Original language	English (US)
Article number	012001
Journal	Nano Futures
Volume	8
Issue number	1
DOIs	https://doi.org/10.1088/2399-1984/ad299a
State	Published - Mar 1 2024

Funding

K Y C is grateful to N A Aadit and A Grimaldi for assistance in the preparation of the figures. K Y C acknowledges support through the National Science Foundation (CAREER Award), Samsung Global Research Outreach (GRO) program, Office of Naval Research (ONR) Young Investigator Program, Multidisciplinary University Research Initiative (MURI) Program and the Semiconductor Research Corporation (SRC). The work of GF was supported under the Project PRIN 2020LWPKH7 funded by the Italian Ministry of University and Research. This work was partially supported by the Science and Engineering Research Council of A*STAR (Agency for Science, Technology and Research) Singapore, under Grant No. A20G9b0135. We thank Jake Love for discussions. D R R acknowledges funding from PRIN 2020LWPKH7, PRIN 20222N9A73, PRIN 20225YF2S4, and D.M. 10/08/2021 n. 1062 (PON Ricerca e Innovazione) funded by the Italian Ministry of University and Research. S S acknowledges supports from JSPS KAKENHI (20H04255) and JST PRESTO (JPMJPR19M4). K E S acknowledges funding from the German Research Foundation (DFG) Project No. 320163632 and the Emergent AI Center funded by the Carl-Zeiss-Stiftung. The work is supported partially by the SpOT-LITE Programme (A*STAR Grant, A18A6b0057) through RIE2020 funds, the National University of Singapore Advanced Research and Technology Innovation Centre (A-0005947-19-00), National Research Foundation (NRF) Singapore (NRF-000214-00), Samsung Electronics\u2019 University R&D Programme, the project PRIN 2020LWPKH7 funded by the Italian Ministry of University and Research and by the European Union\u2019s Horizon 2020 research and innovation program under Grant RadioSpin No. 101017098 and under Grant SWAN-on-chip No. 101070287 HORIZON-CL4-2021-DIGITAL-EMERGING-01. This work was primarily supported by the National Science Foundation (NSF) under Grant Numbers CCF-2106964 and EFMA-2317974. V K S and M C H also acknowledge support from the Department of Energy (DOE) Threadwork Program under Grant Number 8J-30009-0032A and the Laboratory Directed Research and Development Program at Sandia National Laboratories (SNL). SNL is a multi-mission laboratory managed and operated by National Technology and Engineering Solutions of Sandia LLC, a wholly owned subsidiary of Honeywell International Inc. for the U.S. DOE National Nuclear Security Administration under Contract DE-NA0003525. This paper describes objective technical results and analysis. Any subjective views or opinions that might be expressed in the paper do not necessarily represent the views of the U.S. DOE or the United States Government. This work was also supported by Project No. PRIN 2020LWPKH7 funded by the Italian Ministry of University and Research, the Swedish Research Council Framework Grant No. 2016-05980, and the Horizon 2020 research and innovation programme (ERC Advanced Grant No.\u223C835068 \u2018TOPSPIN\u2019). The authors thank Andr\u00E9 Saraiva for the interesting and very fruitful conversations. The authors acknowledge financial support from the Brazilian agencies CNPq (PQ Grant Nos. 307058/2017-4, INCT-IQ 246569/2014-0 and 307910/2019-9). G H A and G P T also acknowledge FAPERJ (Grant Nos. 210.069/2020 and 211.094/2019, respectively) and FAPESP (Grant No. 2021/96774-4). The work of S B in this field has been supported by the National Science Foundation under Grants CCF-2001255 and CCF-2006843. The work of J A C I in this field has been supported by the National Science Foundation under Grants EPMD-2225744, CCF-1910997, and CCF-2006753, as well as Sandia National Laboratories. The work of J S F in this field has been supported by the National Science Foundation Grants CCF-1910800 and CCF-2146439, and the Semiconductor Research Corporation. M D and Y V P acknowledge support from the National Science Foundation under Grant No. ECCS-2229880.

Keywords

computings
memory
nanomaterials
neuromorphic
unconventional

ASJC Scopus subject areas

Bioengineering
General Chemistry
Atomic and Molecular Physics, and Optics
Biomedical Engineering
General Materials Science
Electrical and Electronic Engineering

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10.1088/2399-1984/ad299a

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Finocchio, G., Incorvia, J. A. C., Friedman, J. S., Yang, Q., Giordano, A., Grollier, J., Yang, H., Ciubotaru, F., Chumak, A. V., Naeemi, A. J., Cotofana, S. D., Tomasello, R., Panagopoulos, C., Carpentieri, M., Lin, P., Pan, G., Yang, J. J., Todri-Sanial, A., Boschetto, G., ... Bandyopadhyay, S. (2024). Roadmap for unconventional computing with nanotechnology. Nano Futures, 8(1), Article 012001. https://doi.org/10.1088/2399-1984/ad299a

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title = "Roadmap for unconventional computing with nanotechnology",

abstract = "In the {\textquoteleft}Beyond Moore{\textquoteright}s Law{\textquoteright} 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{\textquoteright}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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author = "Giovanni Finocchio and Incorvia, {Jean Anne C.} and Friedman, {Joseph S.} and Qu Yang and Anna Giordano and Julie Grollier and Hyunsoo Yang and Florin Ciubotaru and Chumak, {Andrii V.} and Naeemi, {Azad J.} and Cotofana, {Sorin D.} and Riccardo Tomasello and Christos Panagopoulos and Mario Carpentieri and Peng Lin and Gang Pan and Yang, {J. Joshua} and Aida Todri-Sanial and Gabriele Boschetto and Kremena Makasheva and Sangwan, {Vinod K.} and Trivedi, {Amit Ranjan} and Hersam, {Mark C.} and Camsari, {Kerem Y.} and McMahon, {Peter L.} and Supriyo Datta and Belita Koiller and Aguilar, {Gabriel H.} and Tempor{\~a}o, {Guilherme P.} and Rodrigues, {Davi R.} and Satoshi Sunada and Karin Everschor-Sitte and Kosuke Tatsumura and Hayato Goto and Vito Puliafito and Johan {\AA}kerman and Hiroki Takesue and Ventra, {Massimiliano Di} and Pershin, {Yuriy V.} and Saibal Mukhopadhyay and Kaushik Roy and Wang, {I. Ting} and Wang Kang and Yao Zhu and Kaushik, {Brajesh Kumar} and Jennifer Hasler and Samiran Ganguly and Ghosh, {Avik W.} and William Levy and Vwani Roychowdhury and Supriyo Bandyopadhyay",

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doi = "10.1088/2399-1984/ad299a",

language = "English (US)",

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Finocchio, G, Incorvia, JAC, Friedman, JS, Yang, Q, Giordano, A, Grollier, J, Yang, H, Ciubotaru, F, Chumak, AV, Naeemi, AJ, Cotofana, SD, Tomasello, R, Panagopoulos, C, Carpentieri, M, Lin, P, Pan, G, Yang, JJ, Todri-Sanial, A, Boschetto, G, Makasheva, K, Sangwan, VK, Trivedi, AR, Hersam, MC, Camsari, KY, McMahon, PL, Datta, S, Koiller, B, Aguilar, GH, Temporão, GP, Rodrigues, DR, Sunada, S, Everschor-Sitte, K, Tatsumura, K, Goto, H, Puliafito, V, Åkerman, J, Takesue, H, Ventra, MD, Pershin, YV, Mukhopadhyay, S, Roy, K, Wang, IT, Kang, W, Zhu, Y, Kaushik, BK, Hasler, J, Ganguly, S, Ghosh, AW, Levy, W, Roychowdhury, V & Bandyopadhyay, S 2024, 'Roadmap for unconventional computing with nanotechnology', Nano Futures, vol. 8, no. 1, 012001. https://doi.org/10.1088/2399-1984/ad299a

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T1 - Roadmap for unconventional computing with nanotechnology

AU - Finocchio, Giovanni

AU - Incorvia, Jean Anne C.

AU - Friedman, Joseph S.

AU - Yang, Qu

AU - Giordano, Anna

AU - Grollier, Julie

AU - Yang, Hyunsoo

AU - Ciubotaru, Florin

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AU - Naeemi, Azad J.

AU - Cotofana, Sorin D.

AU - Tomasello, Riccardo

AU - Panagopoulos, Christos

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AU - Yang, J. Joshua

AU - Todri-Sanial, Aida

AU - Boschetto, Gabriele

AU - Makasheva, Kremena

AU - Sangwan, Vinod K.

AU - Trivedi, Amit Ranjan

AU - Hersam, Mark C.

AU - Camsari, Kerem Y.

AU - McMahon, Peter L.

AU - Datta, Supriyo

AU - Koiller, Belita

AU - Aguilar, Gabriel H.

AU - Temporão, Guilherme P.

AU - Rodrigues, Davi R.

AU - Sunada, Satoshi

AU - Everschor-Sitte, Karin

AU - Tatsumura, Kosuke

AU - Goto, Hayato

AU - Puliafito, Vito

AU - Åkerman, Johan

AU - Takesue, Hiroki

AU - Ventra, Massimiliano Di

AU - Pershin, Yuriy V.

AU - Mukhopadhyay, Saibal

AU - Roy, Kaushik

AU - Wang, I. Ting

AU - Kang, Wang

AU - Zhu, Yao

AU - Kaushik, Brajesh Kumar

AU - Hasler, Jennifer

AU - Ganguly, Samiran

AU - Ghosh, Avik W.

AU - Levy, William

AU - Roychowdhury, Vwani

AU - Bandyopadhyay, Supriyo

PY - 2024/3/1

Y1 - 2024/3/1

N2 - 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.

AB - 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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Roadmap for unconventional computing with nanotechnology

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