Rebooting Our Computing Models

Cadareanu, Patsy; Almudéver, Carmen G.; Khanna, Abhishek; Raychowdhury, Arijit; Datta, Suman; Bertels, Koen; Narayanan, V.; Ventra, Massimiliano Di; Gaillardon, P.-E.

doi:10.23919/date.2019.8715167

Cited by 4 publications

(4 citation statements)

References 48 publications

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“…Recently proposed ideas involve replacing slow digital processing with quick analog processing [36][37][38], to consider any future new physical effects and/or materials [3]. They decrease the physical processing time, only.…”

Section: Using Memristors For Processingmentioning

confidence: 99%

“…Furthermore, he emphasized that it would be unsound to apply his simplified model to modeling a neural operation, where the timing relations are entirely different. Unfortunately, sometimes an implementation based on a different physical effect or material is called a 'model'; for examples, see [3]. Similarly, changing parts of the architectural principles are called models (see cited references in [4] or [5]).…”

Section: Introductionmentioning

confidence: 99%

“…The operating, technological, and utilization characteristics of computers have drastically changed. The aspects of utilization, developing architecture, using different technologies, and new physical effects/materials have been extensively discussed (see, for example, cited references in [3]). However, a new theoretical basis for the detailed analysis of the effects of those changes is still missing; those aspects have been discussed using the classic computing paradigm, which is definitely 'unsound' to describe them.…”

Section: Introductionmentioning

confidence: 99%

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Revising the Classic Computing Paradigm and Its Technological Implementations

Végh¹

2021

Informatics

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Today’s computing is based on the classic paradigm proposed by John von Neumann, three-quarters of a century ago. That paradigm, however, was justified for (the timing relations of) vacuum tubes only. The technological development invalidated the classic paradigm (but not the model!). It led to catastrophic performance losses in computing systems, from the operating gate level to large networks, including the neuromorphic ones. The model is perfect, but the paradigm is applied outside of its range of validity. The classic paradigm is completed here by providing the “procedure” missing from the “First Draft” that enables computing science to work with cases where the transfer time is not negligible apart from the processing time. The paper reviews whether we can describe the implemented computing processes by using the accurate interpretation of the computing model, and whether we can explain the issues experienced in different fields of today’s computing by omitting the wrong omissions. Furthermore, it discusses some of the consequences of improper technological implementations, from shared media to parallelized operation, suggesting ideas on how computing performance could be improved to meet the growing societal demands.

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Section: Using Memristors For Processingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Revising the Classic Computing Paradigm and Its Technological Implementations

Végh¹

2021

Informatics

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“…When thinking about "advances beyond 2020", the solution was expected from the "more efficient implementation of the von Neumann architecture" [13], however. Even when speaking about building up computing from scratch ("rebooting the model" [14]), only implementing different gating technology for the same computing model is meant. However, the paradigm prevents building large neuromorphic systems, too.…”

Section: Limitations Due To the Single Processor Approachmentioning

confidence: 99%

Do we know the operating principles of our computers better than those of our brain?

Végh¹,

Berki²

2020

Preprint

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The increasing interest in understanding the behavior of the biological neural networks, and the increasing utilization of artificial neural networks in different fields and scales, both require a thorough understanding of how neuromorphic computing works. On the one side, the need to program those artificial neuron-like elements, and, on the other side, the necessity for a large number of such elements to cooperate, communicate and compute during tasks, need to be scrutinized to determine how efficiently conventional computing can assist in implementing such systems. Some electronic components bear a surprising resemblance to some biological structures. However, combining them with components that work using different principles can result in systems with very poor efficacy. The paper discusses how the conventional principles, components and thinking about computing limit mimicking the biological systems. We describe what changes will be necessary in the computing paradigms to get closer to the marvelously efficient operation of biological neural networks.

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Introducing Temporal Behavior to Computing Science

Végh¹

2021

Transactions on Computational Science and Computational Intelligence

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Rebooting Our Computing Models

Cited by 4 publications

References 48 publications

Revising the Classic Computing Paradigm and Its Technological Implementations

Revising the Classic Computing Paradigm and Its Technological Implementations

Do we know the operating principles of our computers better than those of our brain?

Introducing Temporal Behavior to Computing Science

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