Level-shifted neural encoded analog-to-digital converter

Aigerim Tankimanova, Akshay Kumar Maan, Alex Pappachen James

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationICECS 2017 - 24th IEEE International Conference on Electronics, Circuits and Systems
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages377-380
Number of pages4
Volume2018-January
ISBN (Electronic)9781538619117
DOIs
Publication statusPublished - Feb 14 2018
Externally publishedYes
Event24th IEEE International Conference on Electronics, Circuits and Systems, ICECS 2017 - Batumi, Georgia
Duration: Dec 5 2017Dec 8 2017

Conference

Conference24th IEEE International Conference on Electronics, Circuits and Systems, ICECS 2017
CountryGeorgia
CityBatumi
Period12/5/1712/8/17

Keywords

  • ADC
  • Hopfield ADC
  • Hopfield neural networks

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Energy Engineering and Power Technology

Fingerprint Dive into the research topics of 'Level-shifted neural encoded analog-to-digital converter'. Together they form a unique fingerprint.

Cite this