Cellular Automaton Belousov-Zhabotinsky Model for Binary Full Adder

© 2017 World Scientific Publishing Company. The continuous increment in the performance of classical computers has been driven to its limit. New ways are studied to avoid this oncoming bottleneck and many answers can be found. An example is the Belousov-Zhabotinsky (BZ) reaction which includes some fundamental and essential characteristics that attract chemists, biologists, and computer scientists. Interaction of excitation wave-fronts in BZ system, can be interpreted in terms of logical gates and applied in the design of unconventional hardware components. Logic gates and other more complicated components have been already proposed using different topologies and particular characteristics. In this study, the inherent parallelism and simplicity of Cellular Automata (CAs) modeling is combined with an Oregonator model of light-sensitive version of BZ reaction. The resulting parallel and computationally-inexpensive model has the ability to simulate a topology that can be considered as a one-bit full adder digital component towards the design of an Arithmetic Logic Unit (ALU)

Heterogeneous memristive crossbar for in-memory computing

It's been quite a while since scientists are seeking for the ancestor of von Neumann computing architecture. Among the most promising candidates, memristor demonstrates advantageous characteristics, which open new pathways for the exploration of advanced computing paradigms. In this work we propose the design of a novel crossbar geometry, which is heterogeneous in terms of its cross-point devices, allowing for the realization of true in-memory digital logic computations. More specifically, it is a combination of two stacked crossbar arrays with a shared intermediate nanowire layer. The variety of available cross-points types allows the execution of parallel memristive logic computations, where the logic state variable is voltage. Moreover, the utilization of insulating patterns in the crossbar arrays, at the expense of a small area-overhead, permits the simultaneous parallel read/write memory operation of two memory words. Memory/logic operation is determined through control signals driven from the peripheral CMOS-based driving circuitry, which also comprises row/column decoders, tri-state drivers, and summing/ sense amplifiers to allow for the proper programming/reading of the memristive cross-pointsPeer ReviewedPostprint (author's final draft

Towards a slime Mould-FPGA interface

© 2015, Korean Society of Medical and Biological Engineering and Springer. Purpose: The plasmodium of slime mouldPhysarum polycephalum: is a multinucleate single celled organism which behaves as a living amorphous unconventional computing substrate. As an excitable, memristive cell that typically assumes a branching or stellate morphology, slime mould is a unique model organism that shares many key properties of mammalian neurons. There are numerous studies that reveal the computing abilities of the plasmodium realized by the formation of tubular networks connecting points of interest. Recent research demonstrating typical responses in electrical behaviour of the plasmodium to certain chemical and physical stimuli has generated interest in creating an interface between.P. polycephalum: and digital logic, with the aim to perform computational tasks with the resulting device.Methods: Through a range of laboratory experiments, wemeasure plasmodial membrane potential via a non-invasive method and use this signal to interface the organism with a digital system.Results: This digital system was demonstrated to perform predefined basic arithmetic operations and is implemented in a field-programmable gate array (FPGA). These basic arithmetic operations, i.e. counting, addition, multiplying, use data that were derived by digital recognition of membrane potential oscillation and are used here to make basic hybrid biologicalartificial sensing devices.Conclusions: We present here a low-cost, energy efficient and highly adaptable platform for developing next-generation machine-organism interfaces. These results are therefore applicable to a wide range of biological/medical and computing/electronics fields

A pragmatic gaze on stochastic resonance based variability tolerant memristance

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Stochastic Resonance (SR) is a nonlinear system specific phenomenon, which was demonstrated to lead to system unexpected (counter-intuitive) performance improvements under certain noise conditions. Memristor, on the other hand, is a fundamentally nonlinear circuit element, thus susceptible to benefit from SR, which recently came in the spotlight of the emerging technologies potential candidates. However, at this time, the variability exhibited by manufactured memristor devices within the same array constitutes the main hurdle in the road towards the commercialisation of memristor-based memories and/or computing units. Thus, in this paper, memristor SR effects are explored, assuming various memristor models, and SR-based memristance range enhancement, tolerant to device-to-device variability, is demonstrated. Our experiments reveal that SR can induce significant R MAX /R MIN ratio increase under up to 60% variability, getting as high as 3.4× for 29 dBm noise power.Peer ReviewedPostprint (author's final draft

Street map analysis with excitable chemical medium

© 2018 American Physical Society. Belousov-Zhabotinsky (BZ) thin layer solution is a fruitful substrate for designing unconventional computing devices. A range of logical circuits, wet electronic devices, and neuromorphic prototypes have been constructed. Information processing in BZ computing devices is based on interaction of oxidation (excitation) wave fronts. Dynamics of the wave fronts propagation is programed by geometrical constraints and interaction of colliding wave fronts is tuned by illumination. We apply the principles of BZ computing to explore a geometry of street networks. We use two-variable Oregonator equations, the most widely accepted and verified in laboratory experiments BZ models, to study propagation of excitation wave fronts for a range of excitability parameters, with gradual transition from excitable to subexcitable to nonexcitable. We demonstrate a pruning strategy adopted by the medium with decreasing excitability when wider and ballistically appropriate streets are selected. We explain mechanics of streets selection and pruning. The results of the paper will be used in future studies of studying dynamics of cities and characterizing geometry of street networks

Intelligent Client Selection for Federated Learning using Cellular Automata

Federated Learning (FL) has emerged as a promising solution for privacy-enhancement and latency minimization in various real-world applications, such as transportation, communications, and healthcare. FL endeavors to bring Machine Learning (ML) down to the edge by harnessing data from million of devices and IoT sensors, thus enabling rapid responses to dynamic environments and yielding highly personalized results. However, the increased amount of sensors across diverse applications poses challenges in terms of communication and resource allocation, hindering the participation of all devices in the federated process and prompting the need for effective FL client selection. To address this issue, we propose Cellular Automaton-based Client Selection (CA-CS), a novel client selection algorithm, which leverages Cellular Automata (CA) as models to effectively capture spatio-temporal changes in a fast-evolving environment. CA-CS considers the computational resources and communication capacity of each participating client, while also accounting for inter-client interactions between neighbors during the client selection process, enabling intelligent client selection for online FL processes on data streams that closely resemble real-world scenarios. In this paper, we present a thorough evaluation of the proposed CA-CS algorithm using MNIST and CIFAR-10 datasets, while making a direct comparison against a uniformly random client selection scheme. Our results demonstrate that CA-CS achieves comparable accuracy to the random selection approach, while effectively avoiding high-latency clients.Comment: 18th IEEE International Workshop on Cellular Nanoscale Networks and their Application