Реалізаційна модель адаптивного суматора для нейроподібних елементів

Abstract

One of the promising areas for the use of neurotechnologies is robotics, namely, systems of technical vision and control systems for mobile robots of various applications. In particular, one of the basic tasks for these systems as part of autonomous robots is the task of object recognizing and determining of the obstacles contours in the movement of mobile robots in a non-deterministic environment. For a compact and reliable implementation of the basic units of these systems, there is no alternative) the use of neural network technologies with to focusing on perspective modern tools (FPGA). It is necessary to take into account the simultaneous perception of visual information, which requires, in turn, parallel spatially distributed processing of large amounts of information. The work proposes the structure of the adaptive adder in composition of artificial neurons, which are basic neural-like elements of different types of neural networks. The proposed pipeline summing device has advanced functionality, as it simulates the operation of the adaptive adder in the formal neuron with the formation of the result of processing taking into account the external bias with the sign, and also performs parallel summation of vector array numbers with the formation of their sum. The proposed adaptive adder has a regular structure consisting of (n+1) cells with almost the same set of units and connections between them, and also implements a spatially distributed process of parallel processing over n input elements of the vector array. All this simplifies the process of placing the adaptive adder in the FPGA chip. The orientation on functionally and technologically powerful FPGA chips allows to get compact and full-featured neurostructures for various purposes, the need for which is extremely important in the control systems of mobile robots.