134 research outputs found
Memcapacitive Devices in Logic and Crossbar Applications
Over the last decade, memristive devices have been widely adopted in
computing for various conventional and unconventional applications. While the
integration density, memory property, and nonlinear characteristics have many
benefits, reducing the energy consumption is limited by the resistive nature of
the devices. Memcapacitors would address that limitation while still having all
the benefits of memristors. Recent work has shown that with adjusted parameters
during the fabrication process, a metal-oxide device can indeed exhibit a
memcapacitive behavior. We introduce novel memcapacitive logic gates and
memcapacitive crossbar classifiers as a proof of concept that such applications
can outperform memristor-based architectures. The results illustrate that,
compared to memristive logic gates, our memcapacitive gates consume about 7x
less power. The memcapacitive crossbar classifier achieves similar
classification performance but reduces the power consumption by a factor of
about 1,500x for the MNIST dataset and a factor of about 1,000x for the
CIFAR-10 dataset compared to a memristive crossbar. Our simulation results
demonstrate that memcapacitive devices have great potential for both Boolean
logic and analog low-power applications
Delay Line as a Chemical Reaction Network
Chemistry as an unconventional computing medium presently lacks a systematic
approach to gather, store, and sort data over time. To build more complicated
systems in chemistries, the ability to look at data in the past would be a
valuable tool to perform complex calculations. In this paper we present the
first implementation of a chemical delay line providing information storage in
a chemistry that can reliably capture information over an extended period of
time. The delay line is capable of parallel operations in a single instruction,
multiple data (SIMD) fashion.
Using Michaelis-Menten kinetics, we describe the chemical delay line
implementation featuring an enzyme acting as a means to reduce copy errors. We
also discuss how information is randomly accessible from any element on the
delay line. Our work shows how the chemical delay line retains and provides a
value from a previous cycle. The system's modularity allows for integration
with existing chemical systems. We exemplify the delay line capabilities by
integration with a threshold asymmetric signal perceptron to demonstrate how it
learns all 14 linearly separable binary functions over a size two sliding
window. The delay line has applications in biomedical diagnosis and treatment,
such as smart drug delivery.Comment: 9 pages, 11 figures, 6 table
COEL: A Web-based Chemistry Simulation Framework
The chemical reaction network (CRN) is a widely used formalism to describe
macroscopic behavior of chemical systems. Available tools for CRN modelling and
simulation require local access, installation, and often involve local file
storage, which is susceptible to loss, lacks searchable structure, and does not
support concurrency. Furthermore, simulations are often single-threaded, and
user interfaces are non-trivial to use. Therefore there are significant hurdles
to conducting efficient and collaborative chemical research. In this paper, we
introduce a new enterprise chemistry simulation framework, COEL, which
addresses these issues. COEL is the first web-based framework of its kind. A
visually pleasing and intuitive user interface, simulations that run on a large
computational grid, reliable database storage, and transactional services make
COEL ideal for collaborative research and education. COEL's most prominent
features include ODE-based simulations of chemical reaction networks and
multicompartment reaction networks, with rich options for user interactions
with those networks. COEL provides DNA-strand displacement transformations and
visualization (and is to our knowledge the first CRN framework to do so), GA
optimization of rate constants, expression validation, an application-wide
plotting engine, and SBML/Octave/Matlab export. We also present an overview of
the underlying software and technologies employed and describe the main
architectural decisions driving our development. COEL is available at
http://coel-sim.org for selected research teams only. We plan to provide a part
of COEL's functionality to the general public in the near future.Comment: 23 pages, 12 figures, 1 tabl
When correlations matter - response of dynamical networks to small perturbations
We systematically study and compare damage spreading for random Boolean and
threshold networks under small external perturbations (damage), a problem which
is relevant to many biological networks. We identify a new characteristic
connectivity , at which the average number of damaged nodes after a large
number of dynamical updates is independent of the total number of nodes . We
estimate the critical connectivity for finite and show that it
systematically deviates from the annealed approximation. Extending the approach
followed in a previous study, we present new results indicating that internal
dynamical correlations tend to increase not only the probability for small, but
also for very large damage events, leading to a broad, fat-tailed distribution
of damage sizes. These findings indicate that the descriptive and predictive
value of averaged order parameters for finite size networks - even for
biologically highly relevant sizes up to several thousand nodes - is limited.Comment: 4 pages, 4 figures. Accepted for the "Workshop on Computational
Systems Biology", Leipzig 200
Material and Physical Reservoir Computing for Beyond CMOS Electronics: Quo Vadis?
Traditional computing is based on an engineering approach that imposes logical states and a computational model upon a physical substrate. Physical or material computing, on the other hand, harnesses and exploits the inherent, naturally-occurring properties of a physical substrate to perform a computation. To do so, reservoir computing is often used as a computing paradigm. In this review and position paper, we take stock of where the field currently stands, delineate opportunities and challenges for future research, and outline steps on how to get material reservoir to the next level. The findings are relevant for beyond CMOS and beyond von Neumann architectures, ML, AI, neuromorphic systems, and computing with novel devices and circuits
Unconventional Computing Catechism
What makes a new paradigm or technology promising? What should science, research, and industry invest money in? Is there a life after CMOS electronics? And will the vacuum tube be back? While one cannot predict the future, one can still learn from the past. Over the last decade, unconventional computing developed into a major new research area with the goal to look beyond existing paradigms. In this Perspective, we reflect on the current state of the field and propose a set of questions that anyone working in unconventional computing should be able to answer in order to assess the potential of new paradigms early on
Learning, Generalization, and Functional Entropy in Random Automata Networks
It has been shown \citep{broeck90:physicalreview,patarnello87:europhys} that
feedforward Boolean networks can learn to perform specific simple tasks and
generalize well if only a subset of the learning examples is provided for
learning. Here, we extend this body of work and show experimentally that random
Boolean networks (RBNs), where both the interconnections and the Boolean
transfer functions are chosen at random initially, can be evolved by using a
state-topology evolution to solve simple tasks. We measure the learning and
generalization performance, investigate the influence of the average node
connectivity , the system size , and introduce a new measure that allows
to better describe the network's learning and generalization behavior. We show
that the connectivity of the maximum entropy networks scales as a power-law of
the system size . Our results show that networks with higher average
connectivity (supercritical) achieve higher memorization and partial
generalization. However, near critical connectivity, the networks show a higher
perfect generalization on the even-odd task
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