Reducing Hardware Costs of a Local Fragmented Control Device in a Neural Network Analog-to-Digital Converter

Abstract

A self-routing analog-to-digital converter based on a single-layer neural network consisting of basic measuring neurons is considered. For the main measuring neuron, a control system is presented that implements self-routing of signals in a neural network using the “echolocation” method when forming an individual meter of the required bit depth. A method is proposed to reduce the hardware costs of implementing a control system by using a local fragmented control device, the elements of which are distributed among neurons and can be combined to control the operation of an analog-to-digital converter. Functional schemes of a local fragmentary control device with separation of counters used in “echolocation” have been developed, on the basis of which models have been built in the Quartus environment, using the latter to estimate hardware costs measured in the number of LUT logic blocks and registers used. The compilation of the project for a programmable logic integrated circuit showed a 20-25% reduction in hardware costs (depending on the parameters of the neural network), compared with using a control device without separating counters. It should be noted that the local fragmented management device makes it easier to scale the network and increase its fault tolerance.