Interstice: Inverter-Based Memristive Neural Networks Discretization for Function Approximation Applications.
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| Title: | Interstice: Inverter-Based Memristive Neural Networks Discretization for Function Approximation Applications. |
|---|---|
| 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 Very Large Scale Integration (VLSI) Systems. Jul2020, Vol. 28 Issue 7, p1578-1588. 11p. |
| Subjects: | Electric inverters, Analog-to-digital converters, Circuit elements |
| Abstract: | In this article, the accuracy of inverter-based memristive neural networks (NNs) for function approximation applications is improved under the presence of process variations. The improvement is achieved by using a design approach, called INTERSTICE (Inverter-based Memristive Neural Networks Dis cretization for Function Approximation Applications), which discretizes the output values by employing a classifier. More precisely, in the INTERSTICE approach, the output range is divided into $K$ subranges where each subrange is considered as a class. To train the classifier, the training samples are labeled where each label shows belonging to a specific class. To evaluate the efficacy of the design technique, some function approximation applications such as BlackScholes, FFT, $K$ -means, and Sobel are considered. Compared to PHAX, a recently published inverter-based memristive NN, INTERSTICE provides lower mean squared error (MSE) values in the presence of memristor and transistor variations. More specifically, the improvements in the mean of MSE ($\mu _{\mathrm {MSE}}$) are in the range of 40%–80% when considering 10% variations in the memristor resistance and transistor parameters. In addition, for most of the benchmarks, INTERSTICE improves the $\mu _{\mathrm {MSE}}$ values of the nominal case (the case where all circuit elements are ideal) compared to PHAX. As another advantage compared to the PHAX, in INTERSTICE, digital outputs can be generated based on the selected classes which eliminates the need for an analog-to-digital converter at the output port connected to the digital part of the system. Finally, achieving lower $\mu _{\mathrm {MSE}}$ values using fewer memristors and consuming lower energy is also attainable with this design approach. [ABSTRACT FROM AUTHOR] |
| Copyright of IEEE Transactions on Very Large Scale Integration (VLSI) Systems 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.) | |
| Database: | Engineering Source |
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| Items | – Name: Title Label: Title Group: Ti Data: Interstice: Inverter-Based Memristive Neural Networks Discretization for Function Approximation Applications. – Name: Author Label: Authors Group: Au 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> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22IEEE+Transactions+on+Very+Large+Scale+Integration+%28VLSI%29+Systems%22">IEEE Transactions on Very Large Scale Integration (VLSI) Systems</searchLink>. Jul2020, Vol. 28 Issue 7, p1578-1588. 11p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Electric+inverters%22">Electric inverters</searchLink><br /><searchLink fieldCode="DE" term="%22Analog-to-digital+converters%22">Analog-to-digital converters</searchLink><br /><searchLink fieldCode="DE" term="%22Circuit+elements%22">Circuit elements</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In this article, the accuracy of inverter-based memristive neural networks (NNs) for function approximation applications is improved under the presence of process variations. The improvement is achieved by using a design approach, called INTERSTICE (Inverter-based Memristive Neural Networks Dis cretization for Function Approximation Applications), which discretizes the output values by employing a classifier. More precisely, in the INTERSTICE approach, the output range is divided into $K$ subranges where each subrange is considered as a class. To train the classifier, the training samples are labeled where each label shows belonging to a specific class. To evaluate the efficacy of the design technique, some function approximation applications such as BlackScholes, FFT, $K$ -means, and Sobel are considered. Compared to PHAX, a recently published inverter-based memristive NN, INTERSTICE provides lower mean squared error (MSE) values in the presence of memristor and transistor variations. More specifically, the improvements in the mean of MSE ($\mu _{\mathrm {MSE}}$) are in the range of 40%–80% when considering 10% variations in the memristor resistance and transistor parameters. In addition, for most of the benchmarks, INTERSTICE improves the $\mu _{\mathrm {MSE}}$ values of the nominal case (the case where all circuit elements are ideal) compared to PHAX. As another advantage compared to the PHAX, in INTERSTICE, digital outputs can be generated based on the selected classes which eliminates the need for an analog-to-digital converter at the output port connected to the digital part of the system. Finally, achieving lower $\mu _{\mathrm {MSE}}$ values using fewer memristors and consuming lower energy is also attainable with this design approach. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of IEEE Transactions on Very Large Scale Integration (VLSI) Systems 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: BibEntity: Identifiers: – Type: doi Value: 10.1109/TVLSI.2020.2991795 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 11 StartPage: 1578 Subjects: – SubjectFull: Electric inverters Type: general – SubjectFull: Analog-to-digital converters Type: general – SubjectFull: Circuit elements Type: general Titles: – TitleFull: Interstice: Inverter-Based Memristive Neural Networks Discretization for Function Approximation Applications. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Vahdat, Shaghayegh – PersonEntity: Name: NameFull: Kamal, Mehdi – PersonEntity: Name: NameFull: Afzali-Kusha, Ali – PersonEntity: Name: NameFull: Pedram, Massoud IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2020 Type: published Y: 2020 Identifiers: – Type: issn-print Value: 10638210 Numbering: – Type: volume Value: 28 – Type: issue Value: 7 Titles: – TitleFull: IEEE Transactions on Very Large Scale Integration (VLSI) Systems Type: main |
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