Roadmap for Unconventional Computing with Nanotechnology
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917598467194880 |
|---|---|
| author | Finocchio, Giovanni Incorvia, Jean Anne C. Friedman, Joseph S. Yang, Qu Giordano, Anna Grollier, Julie Yang, Hyunsoo Ciubotaru, Florin Chumak, Andrii Naeemi, Azad J. Cotofana, Sorin D. Tomasello, Riccardo Panagopoulos, Christos Carpentieri, Mario Lin, Peng Pan, Gang Yang, J. Joshua Todri-Sanial, Aida Boschetto, Gabriele Makasheva, Kremena Sangwan, Vinod K. Trivedi, Amit Ranjan Hersam, Mark C. Camsari, Kerem Y. McMahon, Peter L. Datta, Supriyo Koiller, Belita Aguilar, Gabriel H. Temporão, Guilherme P. Rodrigues, Davi R. Sunada, Satoshi Everschor-Sitte, Karin Tatsumura, Kosuke Goto, Hayato Puliafito, Vito Åkerman, Johan Takesue, Hiroki Di Ventra, Massimiliano Pershin, Yuriy V. Mukhopadhyay, Saibal Roy, Kaushik Wang, I-Ting Kang, Wang Zhu, Yao Kaushik, Brajesh Kumar Hasler, Jennifer Ganguly, Samiran Ghosh, Avik W. Levy, William Roychowdhury, Vwani Bandyopadhyay, Supriyo |
| author_facet | Finocchio, Giovanni Incorvia, Jean Anne C. Friedman, Joseph S. Yang, Qu Giordano, Anna Grollier, Julie Yang, Hyunsoo Ciubotaru, Florin Chumak, Andrii Naeemi, Azad J. Cotofana, Sorin D. Tomasello, Riccardo Panagopoulos, Christos Carpentieri, Mario Lin, Peng Pan, Gang Yang, J. Joshua Todri-Sanial, Aida Boschetto, Gabriele Makasheva, Kremena Sangwan, Vinod K. Trivedi, Amit Ranjan Hersam, Mark C. Camsari, Kerem Y. McMahon, Peter L. Datta, Supriyo Koiller, Belita Aguilar, Gabriel H. Temporão, Guilherme P. Rodrigues, Davi R. Sunada, Satoshi Everschor-Sitte, Karin Tatsumura, Kosuke Goto, Hayato Puliafito, Vito Åkerman, Johan Takesue, Hiroki Di Ventra, Massimiliano Pershin, Yuriy V. Mukhopadhyay, Saibal Roy, Kaushik Wang, I-Ting Kang, Wang Zhu, Yao Kaushik, Brajesh Kumar Hasler, Jennifer Ganguly, Samiran Ghosh, Avik W. Levy, William Roychowdhury, Vwani Bandyopadhyay, Supriyo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2301_06727 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Roadmap for Unconventional Computing with Nanotechnology Finocchio, Giovanni Incorvia, Jean Anne C. Friedman, Joseph S. Yang, Qu Giordano, Anna Grollier, Julie Yang, Hyunsoo Ciubotaru, Florin Chumak, Andrii Naeemi, Azad J. Cotofana, Sorin D. Tomasello, Riccardo Panagopoulos, Christos Carpentieri, Mario Lin, Peng Pan, Gang Yang, J. Joshua Todri-Sanial, Aida Boschetto, Gabriele Makasheva, Kremena Sangwan, Vinod K. Trivedi, Amit Ranjan Hersam, Mark C. Camsari, Kerem Y. McMahon, Peter L. Datta, Supriyo Koiller, Belita Aguilar, Gabriel H. Temporão, Guilherme P. Rodrigues, Davi R. Sunada, Satoshi Everschor-Sitte, Karin Tatsumura, Kosuke Goto, Hayato Puliafito, Vito Åkerman, Johan Takesue, Hiroki Di Ventra, Massimiliano Pershin, Yuriy V. Mukhopadhyay, Saibal Roy, Kaushik Wang, I-Ting Kang, Wang Zhu, Yao Kaushik, Brajesh Kumar Hasler, Jennifer Ganguly, Samiran Ghosh, Avik W. Levy, William Roychowdhury, Vwani Bandyopadhyay, Supriyo Emerging Technologies Applied Physics 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. |
| title | Roadmap for Unconventional Computing with Nanotechnology |
| topic | Emerging Technologies Applied Physics |
| url | https://arxiv.org/abs/2301.06727 |