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Loading-Aware Reliability Improvement of Ultra-Low Power Memristive Neural Networks.

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Title: Loading-Aware Reliability Improvement of Ultra-Low Power Memristive Neural Networks.
Authors: Vahdat, Shaghayegh1 (AUTHOR) vahdat_s@ut.ac.ir, Kamal, Mehdi1 (AUTHOR) mehdikamal@ut.ac.ir, Afzali-Kusha, Ali1 (AUTHOR) afzali@ut.ac.ir, Pedram, Massoud2 (AUTHOR) pedram@usc.edu
Source: IEEE Transactions on Circuits & Systems. Part I: Regular Papers. Aug2021, Vol. 68 Issue 8, p3411-3421. 11p.
Subjects: Circuit elements, Energy consumption, Artificial neural networks
Abstract: In this paper, a method for offline training of inverter-based memristive neural networks (IM-NNs), called ERIM, is presented. In this method, the output voltage of the inverter is modeled very accurately by considering the loading effect of the memristive crossbar. To properly choose the size of each inverter, its output load and the required slope of its voltage transfer characteristic (VTC) for an acceptable level of resiliency to the circuit element non-idealities are taken into account. The efficacy of ERIM is investigated by comparing its accuracy to those of two recently proposed offline training methods for IM-NNs (RIM and PHAX). The study is performed using IRIS, BCW, MNIST, and Fashion MNIST datasets. Simulation results show that 72% (56%) reduction in average energy consumption of the trained networks is achieved compared to RIM (PHAX) thanks to proper sizing of the inverters. In addition, due to the higher accuracy of the NN mathematical model, ERIM results in significant improvements in the match between the results of high-level modeling and HSPICE simulations while exhibiting lower sensitivity to circuit element variations. [ABSTRACT FROM AUTHOR]
Copyright of IEEE Transactions on Circuits & Systems. Part I: Regular Papers is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Loading-Aware Reliability Improvement of Ultra-Low Power Memristive Neural Networks.
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  Data: <searchLink fieldCode="AR" term="%22Vahdat%2C+Shaghayegh%22">Vahdat, Shaghayegh</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> vahdat_s@ut.ac.ir</i><br /><searchLink fieldCode="AR" term="%22Kamal%2C+Mehdi%22">Kamal, Mehdi</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> mehdikamal@ut.ac.ir</i><br /><searchLink fieldCode="AR" term="%22Afzali-Kusha%2C+Ali%22">Afzali-Kusha, Ali</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> afzali@ut.ac.ir</i><br /><searchLink fieldCode="AR" term="%22Pedram%2C+Massoud%22">Pedram, Massoud</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> pedram@usc.edu</i>
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  Data: <searchLink fieldCode="DE" term="%22Circuit+elements%22">Circuit elements</searchLink><br /><searchLink fieldCode="DE" term="%22Energy+consumption%22">Energy consumption</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink>
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  Data: In this paper, a method for offline training of inverter-based memristive neural networks (IM-NNs), called ERIM, is presented. In this method, the output voltage of the inverter is modeled very accurately by considering the loading effect of the memristive crossbar. To properly choose the size of each inverter, its output load and the required slope of its voltage transfer characteristic (VTC) for an acceptable level of resiliency to the circuit element non-idealities are taken into account. The efficacy of ERIM is investigated by comparing its accuracy to those of two recently proposed offline training methods for IM-NNs (RIM and PHAX). The study is performed using IRIS, BCW, MNIST, and Fashion MNIST datasets. Simulation results show that 72% (56%) reduction in average energy consumption of the trained networks is achieved compared to RIM (PHAX) thanks to proper sizing of the inverters. In addition, due to the higher accuracy of the NN mathematical model, ERIM results in significant improvements in the match between the results of high-level modeling and HSPICE simulations while exhibiting lower sensitivity to circuit element variations. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of IEEE Transactions on Circuits & Systems. Part I: Regular Papers is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.1109/TCSI.2021.3084867
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      – Code: eng
        Text: English
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        PageCount: 11
        StartPage: 3411
    Subjects:
      – SubjectFull: Circuit elements
        Type: general
      – SubjectFull: Energy consumption
        Type: general
      – SubjectFull: Artificial neural networks
        Type: general
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      – TitleFull: Loading-Aware Reliability Improvement of Ultra-Low Power Memristive Neural Networks.
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            NameFull: Kamal, Mehdi
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            NameFull: Afzali-Kusha, Ali
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            NameFull: Pedram, Massoud
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            – D: 01
              M: 08
              Text: Aug2021
              Type: published
              Y: 2021
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            – TitleFull: IEEE Transactions on Circuits & Systems. Part I: Regular Papers
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