AMSNet: Analog Memristive System Architecture for Mean-Pooling with Dropout Convolutional Neural Network

Olga Krestinskaya; Adilya Bakambekova; Alex Pappachen James

doi:10.1109/AICAS.2019.8771611

AMSNet: Analog Memristive System Architecture for Mean-Pooling with Dropout Convolutional Neural Network

Olga Krestinskaya, Adilya Bakambekova, Alex Pappachen James

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Scopus citations

Abstract

This work proposes analog hardware implementation of Mean-Pooling Convolutional Neural Network (CNN) with 50% random dropout backpropagation training. We illustrate the effect of variabilities of real memristive devices on the performance of CNN, and tolerance to the input noise. The classification accuracy of CNN is approximately 93% independent on memristor variabilities and input noise. On-chip area and power consumption of analog 180nm CMOS CNN with WOx memristors are 0.09338995mm2 and 3.3992W, respectively.

Original language	English (US)
Title of host publication	Proceedings 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	272-273
Number of pages	2
ISBN (Print)	9781538678848
DOIs	https://doi.org/10.1109/AICAS.2019.8771611
State	Published - Mar 1 2019
Externally published	Yes

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Generated from Scopus record by KAUST IRTS on 2023-09-23

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10.1109/AICAS.2019.8771611

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Krestinskaya, O., Bakambekova, A., & James, A. P. (2019). AMSNet: Analog Memristive System Architecture for Mean-Pooling with Dropout Convolutional Neural Network. In Proceedings 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019 (pp. 272-273). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/AICAS.2019.8771611

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title = "AMSNet: Analog Memristive System Architecture for Mean-Pooling with Dropout Convolutional Neural Network",

abstract = "This work proposes analog hardware implementation of Mean-Pooling Convolutional Neural Network (CNN) with 50% random dropout backpropagation training. We illustrate the effect of variabilities of real memristive devices on the performance of CNN, and tolerance to the input noise. The classification accuracy of CNN is approximately 93% independent on memristor variabilities and input noise. On-chip area and power consumption of analog 180nm CMOS CNN with WOx memristors are 0.09338995mm2 and 3.3992W, respectively.",

author = "Olga Krestinskaya and Adilya Bakambekova and James, {Alex Pappachen}",

note = "Generated from Scopus record by KAUST IRTS on 2023-09-23",

year = "2019",

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doi = "10.1109/AICAS.2019.8771611",

language = "English (US)",

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Krestinskaya, O , Bakambekova, A & James, AP 2019, AMSNet: Analog Memristive System Architecture for Mean-Pooling with Dropout Convolutional Neural Network. in Proceedings 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019. Institute of Electrical and Electronics Engineers Inc., pp. 272-273. https://doi.org/10.1109/AICAS.2019.8771611

AMSNet: Analog Memristive System Architecture for Mean-Pooling with Dropout Convolutional Neural Network. / Krestinskaya, Olga ; Bakambekova, Adilya; James, Alex Pappachen.
Proceedings 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 272-273.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Bakambekova, Adilya

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N1 - Generated from Scopus record by KAUST IRTS on 2023-09-23

PY - 2019/3/1

Y1 - 2019/3/1

N2 - This work proposes analog hardware implementation of Mean-Pooling Convolutional Neural Network (CNN) with 50% random dropout backpropagation training. We illustrate the effect of variabilities of real memristive devices on the performance of CNN, and tolerance to the input noise. The classification accuracy of CNN is approximately 93% independent on memristor variabilities and input noise. On-chip area and power consumption of analog 180nm CMOS CNN with WOx memristors are 0.09338995mm2 and 3.3992W, respectively.

AB - This work proposes analog hardware implementation of Mean-Pooling Convolutional Neural Network (CNN) with 50% random dropout backpropagation training. We illustrate the effect of variabilities of real memristive devices on the performance of CNN, and tolerance to the input noise. The classification accuracy of CNN is approximately 93% independent on memristor variabilities and input noise. On-chip area and power consumption of analog 180nm CMOS CNN with WOx memristors are 0.09338995mm2 and 3.3992W, respectively.

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BT - Proceedings 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

AMSNet: Analog Memristive System Architecture for Mean-Pooling with Dropout Convolutional Neural Network

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