Revising the Classic Computing Paradigm and Its Technological Implementations

Abstract

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.

Towards Generalizing the Information Theory for Neural Communication

“…As pointed out in recent papers [ 17 , 18 , 19 ], in technical computing, the classic computing and communication paradigms behind the reasonable assumptions and formulas (including Shannon’s classic idea on information transfer) prove more and more to be a rough approximation. Good information theory textbooks (such as [ 20 ]; we are using their terminology and notations) exclusively discuss the mathematical theory and formulas describing entropy, both for discrete and analog (i.e., density functions) quantities.…”

“…The present paper suggests extending Shannon’s theory in the direction of including spike-related timing in the messages sent by the neurons. We apply the time-aware computing paradigm [ 17 ] to our specialized neural condenser model.…”

“…The electronics communication tacitly introduces a synchrony signal: a “ring signal” (in different technical implementations) must define the beginning of the message’s arrival. Quite similarly to the role of temporal alignment in the general computing model [ 17 ], the synchronization may be explicit (in the form of a separated physical signal) or implicit. The lack of synchronization in the mathematical theory at least deserves special attention when applying it to electronic or biological communication.…”

“…Our brain comprises orders of magnitude with a higher number of communicating nodes than recent supercomputers. Furthermore, the dispersion [ 17 ] of their communication distances is also by orders of magnitude higher than in supercomputers. A non-spatiotemporal (electronic network) communication model comprising computing nodes, communicating, and cooperating at distances ranging from submicrons to centimeters describes something other than our brain.…”

“…As the elegant investigation [ 48 ] demonstrated, as an implied side result, the result of learning manifests in that the affected neurons change their firing frequency, using the mechanism described above. Based on our time-aware paradigm [ 17 , 27 ], one could find out directly whether it is long- or short-term learning (whether the change in correlates with the change in the corresponding conduction speed or the synaptic strength).…”