A programmable chemical computer with memory and pattern recognition
Juan Manuel Parrilla-Gutierrez,
Abhishek Sharma,
Soichiro Tsuda,
Geoffrey J. T. Cooper,
Gerardo Aragon-Camarasa,
Kevin Donkers and
Leroy Cronin ()
Additional contact information
Juan Manuel Parrilla-Gutierrez: University of Glasgow
Abhishek Sharma: University of Glasgow
Soichiro Tsuda: University of Glasgow
Geoffrey J. T. Cooper: University of Glasgow
Gerardo Aragon-Camarasa: University of Glasgow
Kevin Donkers: University of Glasgow
Leroy Cronin: University of Glasgow
Nature Communications, 2020, vol. 11, issue 1, 1-8
Abstract:
Abstract Current computers are limited by the von Neumann bottleneck, which constrains the throughput between the processing unit and the memory. Chemical processes have the potential to scale beyond current computing architectures as the processing unit and memory reside in the same space, performing computations through chemical reactions, yet their lack of programmability limits them. Herein, we present a programmable chemical processor comprising of a 5 by 5 array of cells filled with a switchable oscillating chemical (Belousov–Zhabotinsky) reaction. Each cell can be individually addressed in the ‘on’ or ‘off’ state, yielding more than 2.9 × 1017 chemical states which arise from the ability to detect distinct amplitudes of oscillations via image processing. By programming the array of interconnected BZ reactions we demonstrate chemically encoded and addressable memory, and we create a chemical Autoencoder for pattern recognition able to perform the equivalent of one million operations per second.
Date: 2020
References: Add references at CitEc
Citations: View citations in EconPapers (9)
Downloads: (external link)
https://www.nature.com/articles/s41467-020-15190-3 Abstract (text/html)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-15190-3
Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/
DOI: 10.1038/s41467-020-15190-3
Access Statistics for this article
Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie
More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().