2,226 research outputs found
Nature-Inspired Interconnects for Self-Assembled Large-Scale Network-on-Chip Designs
Future nano-scale electronics built up from an Avogadro number of components
needs efficient, highly scalable, and robust means of communication in order to
be competitive with traditional silicon approaches. In recent years, the
Networks-on-Chip (NoC) paradigm emerged as a promising solution to interconnect
challenges in silicon-based electronics. Current NoC architectures are either
highly regular or fully customized, both of which represent implausible
assumptions for emerging bottom-up self-assembled molecular electronics that
are generally assumed to have a high degree of irregularity and imperfection.
Here, we pragmatically and experimentally investigate important design
trade-offs and properties of an irregular, abstract, yet physically plausible
3D small-world interconnect fabric that is inspired by modern network-on-chip
paradigms. We vary the framework's key parameters, such as the connectivity,
the number of switch nodes, the distribution of long- versus short-range
connections, and measure the network's relevant communication characteristics.
We further explore the robustness against link failures and the ability and
efficiency to solve a simple toy problem, the synchronization task. The results
confirm that (1) computation in irregular assemblies is a promising and
disruptive computing paradigm for self-assembled nano-scale electronics and (2)
that 3D small-world interconnect fabrics with a power-law decaying distribution
of shortcut lengths are physically plausible and have major advantages over
local 2D and 3D regular topologies
Deep Learning with Photonic Neural Cellular Automata
Rapid advancements in deep learning over the past decade have fueled an
insatiable demand for efficient and scalable hardware. Photonics offers a
promising solution by leveraging the unique properties of light. However,
conventional neural network architectures, which typically require dense
programmable connections, pose several practical challenges for photonic
realizations. To overcome these limitations, we propose and experimentally
demonstrate Photonic Neural Cellular Automata (PNCA) for photonic deep learning
with sparse connectivity. PNCA harnesses the speed and interconnectivity of
photonics, as well as the self-organizing nature of cellular automata through
local interactions to achieve robust, reliable, and efficient processing. We
utilize linear light interference and parametric nonlinear optics for
all-optical computations in a time-multiplexed photonic network to
experimentally perform self-organized image classification. We demonstrate
binary classification of images in the fashion-MNIST dataset using as few as 3
programmable photonic parameters, achieving an experimental accuracy of 98.0%
with the ability to also recognize out-of-distribution data. The proposed PNCA
approach can be adapted to a wide range of existing photonic hardware and
provides a compelling alternative to conventional photonic neural networks by
maximizing the advantages of light-based computing whilst mitigating their
practical challenges. Our results showcase the potential of PNCA in advancing
photonic deep learning and highlights a path for next-generation photonic
computers
Towards heterotic computing with droplets in a fully automated droplet-maker platform
The control and prediction of complex chemical systems is a difficult problem due to the nature of the interactions, transformations and processes occurring. From self-assembly to catalysis and self-organization, complex chemical systems are often heterogeneous mixtures that at the most extreme exhibit system-level functions, such as those that could be observed in a living cell. In this paper, we outline an approach to understand and explore complex chemical systems using an automated droplet maker to control the composition, size and position of the droplets in a predefined chemical environment. By investigating the spatio-temporal dynamics of the droplets, the aim is to understand how to control system-level emergence of complex chemical behaviour and even view the system-level behaviour as a programmable entity capable of information processing. Herein, we explore how our automated droplet-maker platform could be viewed as a prototype chemical heterotic computer with some initial data and example problems that may be viewed as potential chemically embodied computations
Non-classical computing: feasible versus infeasible
Physics sets certain limits on what is and is not computable. These limits are very far from having been reached by current technologies. Whilst proposals for hypercomputation are almost certainly infeasible, there are a number of non classical approaches that do hold considerable promise. There are a range of possible architectures that could be implemented on silicon that are distinctly different from the von Neumann model. Beyond this, quantum simulators, which are the quantum equivalent of analogue computers, may be constructable in the near future
Quantum-dot Cellular Automata: Review Paper
Quantum-dot Cellular Automata (QCA) is one of the most important discoveries that will be the successful alternative for CMOS technology in the near future. An important feature of this technique, which has attracted the attention of many researchers, is that it is characterized by its low energy consumption, high speed and small size compared with CMOS. Inverter and majority gate are the basic building blocks for QCA circuits where it can design the most logical circuit using these gates with help of QCA wire. Due to the lack of availability of review papers, this paper will be a destination for many people who are interested in the QCA field and to know how it works and why it had taken lots of attention recentl
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A Novel Reconfiguration Scheme in Quantum-Dot Cellular Automata for Energy Efficient Nanocomputing
Quantum-Dot Cellular Automata (QCA) is currently being investigated as an alternative to CMOS technology. There has been extensive study on a wide range of circuits from simple logical circuits such as adders to complex circuits such as 4-bit processors. At the same time, little if any work has been done in considering the possibility of reconfiguration to reduce power in QCA devices. This work presents one of the first such efforts when considering reconfigurable QCA architectures which are expected to be both robust and power efficient. We present a new reconfiguration scheme which is highly robust and is expected to dissipate less power with respect to conventional designs. An adder design based on the reconfiguration scheme will be presented in this thesis, with a detailed power analysis and comparison with existing designs. In order to overcome the problems of routing which comes with reconfigurability, a new wire crossing mechanism is also presented as part of this thesis
A probabilistic chemical programmable computer
The exponential growth of the power of modern digital computers is based upon
the miniaturisation of vast nanoscale arrays of electronic switches, but this
will be eventually constrained by fabrication limits and power dissipation.
Chemical processes have the potential to scale beyond these limits performing
computations through chemical reactions, yet the lack of well-defined
programmability limits their scalability and performance. We present a hybrid
digitally programmable chemical array as a probabilistic computational machine
that uses chemical oscillators partitioned in interconnected cells as a
computational substrate. This hybrid architecture performs efficient
computation by distributing between chemical and digital domains together with
error correction. The efficiency is gained by combining digital with
probabilistic chemical logic based on nearest neighbour interactions and
hysteresis effects. We demonstrated the implementation of one- and two-
dimensional Chemical Cellular Automata and solutions to combinatorial
optimization problems.Comment: 20 page manuscript, 6 figures, 112 page supplementary volum
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