Introducing temporal behavior to computing science

TL;DR

This paper introduces a temporal logic to computing science, addressing the limitations of the classic von Neumann model by incorporating component timing, which explains modern computational challenges and guides more efficient design.

Contribution

It presents a novel temporal logic framework for computing, integrating timing considerations into the foundational model, and revises classic design principles for improved efficiency.

Findings

Temporal behavior explains high power consumption.

Timing considerations clarify neural network training durations.

Revising design principles leads to more efficient computing architectures.

Abstract

The abstraction introduced by von Neumann correctly reflected the state of the art 70 years ago. Although it omitted data transmission time between components of the computer, it served as an excellent base for classic computing for decades. Modern computer components and architectures, however, require to consider their temporal behavior: data transmission time in contemporary systems may be higher than their processing time. Using the classic paradigm leaves some issues unexplained, from enormously high power consumption to days-long training of artificial neural networks to failures of some cutting-edge supercomputer projects. The paper introduces the up to now missing timely behavior (a temporal logic) into computing, while keeps the solid computing science base. The careful analysis discovers that with considering the timely behavior of components and architectural principles, the mystic issues have a trivial explanation. Some classic design principles must be revised, and the temporal logic enables us to design a more powerful and efficient computing.