Level-Shifted Neural Encoded Analog-to-Digital Converter

TL;DR

This paper introduces a scalable Hopfield neural-network based ADC that improves resolution while maintaining manageable voltage ranges, using parallel 2-bit quantizers and a neural encoder to reduce quantization errors.

Contribution

It proposes a novel scalable neural ADC architecture with level-shifting and neural encoding to enhance resolution and reduce errors compared to traditional Hopfield NADC designs.

Findings

Scalable design maintains voltage range with increased bits.

Parallel 2-bit quantizers improve resolution.

Neural encoder reduces quantization errors.

Abstract

This paper presents the new approach in implementation of analog-to-digital converter (ADC) that is based on Hopfield neural-network architecture. Hopfield neural ADC (NADC) is a type of recurrent neural network that is effective in solving simple optimization problems, such as analog-to-digital conversion. The main idea behind the proposed design is to use multiple 2-bit Hopfield NADCs operating as quantizers in parallel, where analog input signal to each successive 2-bit Hopfield ADC block is passed through a voltage level shifter. This is followed by a neural network encoder to remove the quantization errors. In traditional Hopfield NADC based designs, increasing the number of bits could require proper scaling of the network parameters, in particular digital output operating region. Furthermore, the resolution improvement of traditional Hopfield NADC creates digital error that increases with the increasing number of bits. The proposed design is scalable in number of bits and number of quantization levels, and can maintain the magnitude of digital output code within a manageable operating voltage range.