Abstract
The paper addresses classification and formal definition of neurocomputer systems for robotic complexes, based on the types of associations among their elements. We suggest analytical expressions for performance evaluation in neural computer information processing, aimed at development of methods, algorithms and software that optimize such systems.
Highlights
It is obvious that productivity of computing devices and systems is insufficient for solving a number of tasks, such as processing of video and audio data, real-time management robotics, recognition of images, forecasting, optimization, and artificial intellect tasks for robotics
The problem is due to several causes: it is impossible to increase the frequencies of computing devices because of the “technological restrictions” we currently face; we lack effective methods, algorithms and software solutions for parallelization of operations when multiprocessor and multinuclear systems are used; we have a limited number of generic methods, algorithms and software means of parallelization, when employing specialized computing devices
The theoretical and practical results of the study have been used for development of a specialized neurocomputing robotic device based on neuroprocessors for automated control of modules in electric-mechanical systems in a near real-time mode [10-13]
Summary
It is obvious that productivity of computing devices and systems is insufficient for solving a number of tasks, such as processing of video and audio data, real-time management robotics, recognition of images, forecasting, optimization, and artificial intellect tasks for robotics. Δ is the controller (governing node) of the CPCS; Sw is the structure of the CNPCS; a is the hardware architecture of each CNPCS node; h is the software architecture of each CNPCS node; Ttr : CL ∪{Δ}×CL ∪{Δ} defines the average throughput between each pair of computing clusters (bytes/sec); Tl : CL ∪{Δ}× CL ∪{Δ} defines the latency, i.e., time required to initialize messages, send and receive data, etc. Let us look into the Ttr and Tl sets for CNPCSs with distributed structures In such cases, the total data transfer times are computed as: Ts = Tlij + Ttrpo + Ttrop + Tlij + Tl po + Tlop i =1 i =1. We assume the total time loss in data transfer to be the following: Tsс = Ttr + Tl + Ttrс + Tlc. Using the above ratios, we can define the analytical expressions for evaluating the chief criterion of effectiveness: performance of neurocomputer systems for various structures: pipeline, vector, pipeline-vector, and vector-pipeline. The processing time in a distributed or cloud system does not differ from the processing time in a parallel system
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