Ulmann, Bernd · CERN Document Server (European Organization for Nuclear Research) · 2013
This book is a comprehensive introduction to analog computing. As most textbooks about this powerful computing paradigm date back to the 1960s and 1970s, it fills a void and forges a bridge from the early days of analog computing to future applications. The idea of analog computing is not new. In fact, this computing paradigm is nearly forgotten, although it offers a path to both high-speed and low-power computing, which are in even more demand now than they were back in the heyday of electronic analog computers.
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C.S. Walker · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · 1958
An analog eomputer, or simulator, is discussed in an elementary manner. A few of the operations which can be performed by an analog facility are described. The operation of a computer as a reactor simulator is discussed at length. (M.H.R.)
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Umberto Meneghetti · History of mechanism and machine science/History of mechanism and machine sciense · 2025
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Bernd Ulmann · 2022
Analog computing is one of the main pillars of Unconventional Computing. Almost forgotten for decades, we now see an ever-increasing interest in electronic analog computing because it offers a path to high-performance and highly energy-efficient computing. These characteristics are of great importance in a world where vast amounts of electric energy are consumed by today's computer systems. Analog computing can deliver efficient solutions to many computing problems, ranging from general purpose analog computation to specialised systems like analog artificial neural networks. The book "Analog Computing" has established itself over the past decade as the standard textbook on the subject and has been substantially extended in this second edition, which includes more than 300 additional bibliographical entries, and has been expanded in many areas to include much greater detail. These enhancements will confirm this book's status as the leading work in the field. It covers the history of analog computing from the Antikythera Mechanism to recent electronic analog computers and uses a wide variety of worked examples to provide a comprehensive introduction to programming analog computers. It also describes hybrid computers, digital differential analysers, the simulation of analog computers, stochastic computers, and provides a comprehensive treatment of classic and current analog computer applications. The last chapter looks into the promising future of analog computing.
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Ulmann, Bernd 1970-
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Bernd Ulmann · 2026
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Bernd Ulmann · 2006
The following slides are part of the workshop ”Analog Computing ” which was held at the VCFE-2006 in Munich. This workshop gives an introduction to the art of analog computing by outlining some examples ranging from a mass-spring-damper system to a bouncing ball in a box. All examples have been programmed and executed on real analog computers like the Telefunken RA742, etc. Workshop – Analog Computing 19-APR-2006 ulmann@vaxman.de
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NASA/Glenn Research Center · Bulletin of Miscellaneous Information (Royal Gardens Kew) · 1960
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Alex Pappachen James · River Publishers eBooks · 2023
Analog memristive computing explains on neuromorphic computing applications and in-memory computing strategies governed by analog computing using memristive devices. This chapter concentrates on the relevance and requirement of analog and analog computing using memristor devices which are widely used in in-memory as well as neuromorphic computing applications. Lots of systems today are basically inspired by the human brain because of its peculiarities like low power, learning capability and relatively much smaller size. Traditionally, the bottleneck has always been the data transmission between the memory and processing that we can deal with to some extent. At the same time, also have in- memory computing approaches where it most closely relates the idea of the brain. The inspiration of memristive computing points to the human brain, considered as memory and connection of memories, characterised by the presence of highly dense neuron cells.
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Matthew R. Kucic, P. Hasler · UPT. Syiah Kuala University Library (Syiah Kuala University) · 2004
Analog Computing Arrays (ACAs) provide a computation system capable of performing a large number of multiply and add operations in an analog form. This system can therefore implement several computation algorithms that are currently realized using Digital Signal Processors (DSPs) who have an analogues accumulate and add functionality. DSPs are generally preferred for signal processing because they provide an environment that permits programmability once fabricated. ACA systems propose to offer similar functionality by providing a programmable and reconfigurable analog system. ACAs inherent parallelism and analog efficiency present several advantages over DSP implementations of the same systems.\n\nThe computation power of an ACA system is directly proportional to the number of computing elements used in the system. Array size is limited by the number of computation elements that can be managed in an array. This number is continually growing and as a result, is permitting the realization of signal processing systems such as real-time speech recognition, image processing, and many other matrix like computation systems. \n\nThis research provides a systematic process to implement, program, and use the computation elements in large-scale Analog Computing Arrays. This infrastructure facilitates the incorporation of ACA without the current headaches of programming large arrays of analog floating-gates from off-chip, currently using multiple power supplies, expensive FPGA controllers/computers, and custom Printed Circuit Board (PCB) systems. Proof of the flexibility and usefulness of ACAs has been demonstrated by the construction of two systems, an Analog Fourier Transform and a Vector Quantizer.
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Stanley Rogers · Industrial & Engineering Chemistry · 1958
ADVERTISEMENT RETURN TO ISSUEPREVArticleNEXTAnalog Computers and ComputingStanley RogersCite this: Ind. Eng. Chem. 1958, 50, 11, 1636–1640Publication Date (Print):November 1, 1958Publication History Published online1 May 2002Published inissue 1 November 1958https://pubs.acs.org/doi/10.1021/ie50587a032https://doi.org/10.1021/ie50587a032research-articleACS PublicationsRequest reuse permissionsArticle Views54Altmetric-Citations-LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail Other access options Get e-Alerts
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Galicia Martínez, Asier · Communities in ADDI (University of the Basque Country) · 2020
[EN] We reviewed the paradigm of quantum computation, analysing its strengths and some of the most relevant algorithms, shuch as Shor's algorithm and Grover's algorithm. We also developed a new algorithm in the digital-analog quantum computation framework which aims at enhancing connectivity between qubits in quantum platforms.
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Daniel Fu, Shalin Shah, Tianqi Song et al. · Methods in molecular biology · 2018
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ANALOG Computing Magazine · Bulletin of Miscellaneous Information (Royal Gardens Kew)
ANALOG Computing Telecommunications System (TCS) Guide For background on the ANALOG TCS, listen to this interview with Tom Hudson
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Qi Zhang, Narendra N. Hegade, Alejandro Gomez Cadavid et al. · npj Unconventional Computing · 2026
Harnessing the analog capacity of quantum processors at the algorithmic level is key to solving computationally hard problems. Neutral atoms offer analog capabilities supporting hundreds of qubits, but state-of-the-art adiabatic protocol struggles with nonadiabatic errors, restricting scalability due to finite coherence times. To address this, we propose and experimentally demonstrate a tailored analog counterdiabatic quantum computing (ACQC) protocol to enhance the computational capabilities of neutral atoms by mitigating non-adiabatic transitions and facilitating rapid and high-quality solutions. We apply it to solve the maximum independent set problem with up to 100 qubits, achieving over 3-fold speedup in convergence time and solution quality within a short evolution time of the processor, as compared to adiabatic method. Our method shows scalibility of the application of neutral atom processors establishing ACQC as a promising pathway toward quantum advantage for real-world industrial applications.
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Elsevier eBooks · 1970
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Mahdi Soleymani, Hessam Mahdavifar, A. Salman Avestimehr · IEEE Journal on Selected Areas in Information Theory · 2021
A distributed computing scenario is considered, where the computational power of a set of worker nodes is used to perform a certain computation task over a dataset that is dispersed among the workers. Lagrange coded computing (LCC), proposed by Yu et al., leverages the well-known Lagrange polynomial to perform polynomial evaluation of the dataset in such a scenario in an efficient parallel fashion while keeping the privacy of data amidst possible collusion of workers. This solution relies on quantizing the data into a finite field, so that Shamir's secret sharing, as one of its main building blocks, can be employed. Such a solution, however, is not properly scalable with the size of dataset, mainly due to computation overflows. To address such a critical issue, we propose a novel extension of LCC to the analog domain, referred to as analog LCC (ALCC). All the operations in the proposed ALCC protocol are done over the infinite fields of \mathbb R/ \mathbb C but for practical implementations floating-point numbers are used. We characterize the privacy of data in ALCC, against any subset of colluding workers up to a certain size, in terms of the distinguishing security (DS) and the mutual information security (MIS) metrics. Also, the accuracy of outcome is characterized in a practical setting assuming operations are performed using floating-point numbers. Consequently, a fundamental trade-off between the accuracy of the outcome of ALCC and its privacy level is observed and is numerically evaluated. Moreover, we implement the proposed scheme to perform matrix-matrix multiplication over a batch of matrices. It is observed that ALCC is superior compared to the state-of-the-art LCC, implemented using fixed-point numbers, assuming both schemes use an equal number of bits to represent data symbols.
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Johannes Schemmel, Sebastian Billaudelle, Phillip Dauer et al. · arXiv (Cornell University) · 2020
This paper presents the concepts behind the BrainScales (BSS) accelerated analog neuromorphic computing architecture. It describes the second-generation BrainScales-2 (BSS-2) version and its most recent in-silico realization, the HICANN-X Application Specific Integrated Circuit (ASIC), as it has been developed as part of the neuromorphic computing activities within the European Human Brain Project (HBP). While the first generation is implemented in an 180nm process, the second generation uses 65nm technology. This allows the integration of a digital plasticity processing unit, a highly-parallel micro processor specially built for the computational needs of learning in an accelerated analog neuromorphic systems. The presented architecture is based upon a continuous-time, analog, physical model implementation of neurons and synapses, resembling an analog neuromorphic accelerator attached to build-in digital compute cores. While the analog part emulates the spike-based dynamics of the neural network in continuous-time, the latter simulates biological processes happening on a slower time-scale, like structural and parameter changes. Compared to biological time-scales, the emulation is highly accelerated, i.e. all time-constants are several orders of magnitude smaller than in biology. Programmable ion channel emulation and inter-compartmental conductances allow the modeling of nonlinear dendrites, back-propagating action-potentials as well as NMDA and Calcium plateau potentials. To extend the usability of the analog accelerator, it also supports vector-matrix multiplication. Thereby, BSS-2 supports inference of deep convolutional networks as well as local-learning with complex ensembles of spiking neurons within the same substrate.
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Mahdi Soleymani, Hessam Mahdavifar, A. Salman Avestimehr · arXiv (Cornell University) · 2020
A distributed computing scenario is considered, where the computational power of a set of worker nodes is used to perform a certain computation task over a dataset that is dispersed among the workers. Lagrange coded computing (LCC), proposed by Yu et al., leverages the well-known Lagrange polynomial to perform polynomial evaluation of the dataset in such a scenario in an efficient parallel fashion while keeping the privacy of data amidst possible collusion of workers. This solution relies on quantizing the data into a finite field, so that Shamir's secret sharing, as one of its main building blocks, can be employed. Such a solution, however, is not properly scalable with the size of dataset, mainly due to computation overflows. To address such a critical issue, we propose a novel extension of LCC to the analog domain, referred to as analog LCC (ALCC). All the operations in the proposed ALCC protocol are done over the infinite fields of R/C but for practical implementations floating-point numbers are used. We characterize the privacy of data in ALCC, against any subset of colluding workers up to a certain size, in terms of the distinguishing security (DS) and the mutual information security (MIS) metrics. Also, the accuracy of outcome is characterized in a practical setting assuming operations are performed using floating-point numbers. Consequently, a fundamental trade-off between the accuracy of the outcome of ALCC and its privacy level is observed and is numerically evaluated. Moreover, we implement the proposed scheme to perform matrix-matrix multiplication over a batch of matrices. It is observed that ALCC is superior compared to the state-of-the-art LCC, implemented using fixed-point numbers, assuming both schemes use an equal number of bits to represent data symbols.
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Pickett, Nicole Christine, Chun, Lauren Cathy Sueko, Nguyen, Dillon Ryan · DigitalCommons - CalPoly (California State Polytechnic University) · 2023
This project entails designing, simulating, and verifying analog circuits that can perform essential computing functions for power systems applications. The project aims to remedy critical challenges associated with handling calculations digitally, namely, time and power. This project's scope includes creating a library of circuits in SPICE that can be used to model and simulate complex mathematical equations. From these SPICE models, the circuit can be constructed physically, where the solution can be generated in less time using less power than doing the computation digitally. The performance and efficiency of analog computing will be measured and compared to conventional digital methods.
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