Brownian Circuits

Peper, Ferdinand; Lee, Jia; Carmona, Josep; Cortadella, Jordi; Morita, Kenichi

doi:10.1145/2422094.2422097

Cited by 38 publications

(18 citation statements)

References 26 publications

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“…Bio-and nano-systems are typically governed by probabilistic interactions, and for this reason alone they are best timed asynchronously, because it is difficult to guarantee that interactions take place within the interval defined by a clock. Brownian circuits [36,51] have been developed with probabilistic interactions in mind, but they are mostly geared towards signals consisting of single particles.…”

Section: And Natural Computing?mentioning

confidence: 99%

The End of Moore’s Law: Opportunities for Natural Computing?

Peper

2017

New Gener. Comput.

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The impending end of Moore's Law has started a rethinking of the way computers are built and computation is done. This paper discusses two directions that are currently attracting much attention as future computation paradigms: the merging of logic and memory, and brain-inspired computing. Natural computing has been known for its innovative methods to conduct computation, and as such may play an important role in the shaping of the post-Moore era.

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Section: And Natural Computing?mentioning

confidence: 99%

The End of Moore’s Law: Opportunities for Natural Computing?

Peper

2017

New Gener. Comput.

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“…It has been shown that computational universality in Brownian circuits can be achieved by very limited sets of primitives. 10,11) In this paper, we will use the set in 10) .…”

Section: Brownian Circuitsmentioning

confidence: 99%

“…( q1, (11,12,12,11,12,12), (20,1,16,20,16,16) ) → ( q1, ( , 17, 17, , 17, 17) ) ( q1, (11,12,12,11,12,12), (20, 16, 1, 20, 16, 16) ) → ( q1, ( , 17, 17, , 17, 17) ) ( q1, (11,12,12,11,12,12), (20, 1, 16, 20, 1, 16) ) → ( q2, ( , 13, 17, , 13, 17) ) ( q1, (11,12,12,11,12,12), (20, 16, 1, 20, 16, 1) ) → ( q2, ( , 17, 13, , 17, 13) ) ( q2, (11,13,17,11,13,17), (20,14,16,20,14,16) ) → ( q2, ( , , 15, , , 15) ) ( q2, (11,17,13,11,17,13), (20,16,14,20,16,14) ) → ( q2, ( , 15, , , 15, ) ) ( ...…”

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confidence: 99%

Swarm Networks in Brownian Environments

et al. 2015

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Swarm Networks are a generalization of Cellular Automata, in which the neighborhoods and functionalities of cells are determined by the presence or absence of connections between cells. This paper presents a Swarm Network in which connections can be changed dynamically, and in which the cells (called "agents") are subject to Brownian motion. According to these characteristics, the model mimics behavior typically encountered in biological organisms. We show that this model is capable of universal computation by constructing a universal Brownian circuit based on it.

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“…A wide range of computing proposals are presented which utilize noise for increased performance efficiency in computing [ 1 , 2 , 3 , 4 , 5 , 6 , 7 ]. Among these novel approaches, an emerging paradigm stands out as it implements Brownian motion in electronic circuit architectures that can exploit fluctuations to reduce energy dissipation in computing [ 8 , 9 , 10 ]. Brownian circuit technology is designed to efficiently and reliably perform primitive logic operations in the presence of noise and fluctuations, and Single-Electron Transistor (SET) technology is proposed as an appropriate technology-base to realize these circuits, as it has been studied extensively and meets the conditions needed to process token-based signals that are subject to fluctuations [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Physical Limitations on Fundamental Efficiency of SET-Based Brownian Circuits

Ercan

Sütgöl

Özhan

2021

Entropy

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Brownian circuits are based on a novel computing approach that exploits quantum fluctuations to increase the efficiency of information processing in nanoelectronic paradigms. This emerging architecture is based on Brownian cellular automata, where signals propagate randomly, driven by local transition rules, and can be made to be computationally universal. The design aims to efficiently and reliably perform primitive logic operations in the presence of noise and fluctuations; therefore, a Single Electron Transistor (SET) device is proposed to be the most appropriate technology-base to realize these circuits, as it supports the representation of signals that are token-based and subject to fluctuations due to the underlying tunneling mechanism of electric charge. In this paper, we study the physical limitations on the energy efficiency of the Single-Electron Transistor (SET)-based Brownian circuit elements proposed by Peper et al. using SIMON 2.0 simulations. We also present a novel two-bit sort circuit designed using Brownian circuit primitives, and illustrate how circuit parameters and temperature affect the fundamental energy-efficiency limitations of SET-based realizations. The fundamental lower bounds are obtained using a physical-information-theoretic approach under idealized conditions and are compared against SIMON 2.0 simulations. Our results illustrate the advantages of Brownian circuits and the physical limitations imposed on their SET-realizations.

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Brownian Circuits

Cited by 38 publications

References 26 publications

The End of Moore’s Law: Opportunities for Natural Computing?

The End of Moore’s Law: Opportunities for Natural Computing?

Swarm Networks in Brownian Environments

Physical Limitations on Fundamental Efficiency of SET-Based Brownian Circuits

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