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 language | English |
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Title of host publication | ICECS 2017 - 24th IEEE International Conference on Electronics, Circuits and Systems |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 377-380 |
Number of pages | 4 |
Volume | 2018-January |
ISBN (Electronic) | 9781538619117 |
DOIs | |
Publication status | Published - Feb 14 2018 |
Externally published | Yes |
Event | 24th IEEE International Conference on Electronics, Circuits and Systems, ICECS 2017 - Batumi, Georgia Duration: Dec 5 2017 → Dec 8 2017 |
Conference
Conference | 24th IEEE International Conference on Electronics, Circuits and Systems, ICECS 2017 |
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Country | Georgia |
City | Batumi |
Period | 12/5/17 → 12/8/17 |
Keywords
- ADC
- Hopfield ADC
- Hopfield neural networks
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Energy Engineering and Power Technology