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Real-Time Trainable Data Converters for General Purpose Applications

Published: 17 July 2018 Publication History

Abstract

Data converters are ubiquitous in data-abundant systems, where they are heterogeneously distributed across the analog-digital interface. Unfortunately, conventional data converters trade off speed, power, and accuracy. Furthermore, intrinsic real-time and post-silicon variations dramatically degrade their performance. In this paper, we employ novel neuro-inspired approaches to design smart data converters that could be trained in real-time for general purpose applications, using machine learning algorithms and artificial neural network architectures. Our approach integrates emerging memristor technology with CMOS. This concept will pave the way towards adaptive interfaces with the continuous varying conditions of data driven applications.

References

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P. Kinget et al., "Impact of Transistor Mismatch on the Speed-Accuracy-Power Trade-Off of Analog CMOS Circuits," CICC, pp. 333--336, May 1996.
[2]
B. Murmann, "ADC Performance Survey 1997-2017," {Online}. Available: http://web.stanford.edu/~murmann/adcsurvey.html.
[3]
L. Danial et al., "DIDACTIC: A Data-Intelligent Digital-to-Analog Converter with a Trainable Integrated Circuit using Memristors," JETCAS, Vol. 8, No. 1, pp. 146--158, March 2018.
[4]
L. Danial et al., "Breaking Through the Speed-Power-Accuracy in ADCs using a Memristive Neuromorphic Architecture," TETCI (in press).
[5]
L. Gao et al., "Digital-to-Analog and Analog-to-Digital Conversion with Metal Oxide Memristors for Ultra-Low Power Computing," NANOARCH, pp. 19--22, July 2013.
[6]
M. Prezioso et al., "Training and Operation of an Integrated Neuromorphic Network Based on Metal-Oxide Memristors," Nature, Vol. 521, No. 7550, pp. 61--64, May 2015.
[7]
D. Soudry et al., "Memristor-Based Multilayer Neural Networks with Online Gradient Descent Training,"TNNLS, Vol. 26, No. 10, pp. 2408--2421, October 2015.

Cited By

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  • (2024)Machine Learning Based Delta Sigma Modulator Using Memristor for Neuromorphic ComputingIntelligent Computing for Sustainable Development10.1007/978-3-031-61298-5_7(82-95)Online publication date: 24-May-2024
  • (2022)Pipelined Memristive Analog-to-Digital Converter With Self-Adaptive Weight TuningIEEE Journal on Emerging and Selected Topics in Circuits and Systems10.1109/JETCAS.2022.322108312:4(913-921)Online publication date: Dec-2022
  • (2022)Neuromorphic Data Converters Using MemristorsEmerging Computing: From Devices to Systems10.1007/978-981-16-7487-7_8(245-290)Online publication date: 9-Jul-2022
  • Show More Cited By

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  1. Real-Time Trainable Data Converters for General Purpose Applications

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    cover image ACM Conferences
    NANOARCH '18: Proceedings of the 14th IEEE/ACM International Symposium on Nanoscale Architectures
    July 2018
    176 pages
    ISBN:9781450358156
    DOI:10.1145/3232195
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 17 July 2018

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    Author Tags

    1. Analog-to-digital conversion
    2. adaptive systems
    3. digital-to-analog conversion
    4. machine learning
    5. memristors
    6. neuromorphic computing
    7. reconfigurable architectures

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    NANOARCH '18 Paper Acceptance Rate 30 of 56 submissions, 54%;
    Overall Acceptance Rate 55 of 87 submissions, 63%

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    Cited By

    View all
    • (2024)Machine Learning Based Delta Sigma Modulator Using Memristor for Neuromorphic ComputingIntelligent Computing for Sustainable Development10.1007/978-3-031-61298-5_7(82-95)Online publication date: 24-May-2024
    • (2022)Pipelined Memristive Analog-to-Digital Converter With Self-Adaptive Weight TuningIEEE Journal on Emerging and Selected Topics in Circuits and Systems10.1109/JETCAS.2022.322108312:4(913-921)Online publication date: Dec-2022
    • (2022)Neuromorphic Data Converters Using MemristorsEmerging Computing: From Devices to Systems10.1007/978-981-16-7487-7_8(245-290)Online publication date: 9-Jul-2022
    • (2020)A Pipelined Memristive Neural Network Analog-to-Digital Converter2020 IEEE International Symposium on Circuits and Systems (ISCAS)10.1109/ISCAS45731.2020.9181108(1-5)Online publication date: Oct-2020
    • (2019)Logarithmic Neural Network Data Converters using Memristors for Biomedical Applications2019 IEEE Biomedical Circuits and Systems Conference (BioCAS)10.1109/BIOCAS.2019.8919068(1-4)Online publication date: Oct-2019

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