Hardware-efficient custom floating-point dot-product processor for LSTM.
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| Title: | Hardware-efficient custom floating-point dot-product processor for LSTM. |
|---|---|
| Authors: | Venkatesh, Shreya1 (AUTHOR), Sindhu, R1 (AUTHOR), Hallur, Ashish G1 (AUTHOR), Sharma, Karra Shivam1 (AUTHOR), V, Arunachalam1 (AUTHOR) varunachalam@vit.ac.in |
| Source: | International Journal of Electronics Letters. Jun2026, Vol. 14 Issue 2, p242-254. 13p. |
| Subjects: | Long short-term memory, Floating-point arithmetic, Recurrent neural networks, Classification algorithms, Arithmetic |
| Abstract: | This paper presents a hardware-efficient implementation of dot-product operations tailored for Long Short-Term Memory (LSTM) Recurrent Neural Networks (RNNs) using custom floating-point (CFP) format. The proposed design introduces a precision-aware arithmetic scheme that balances accuracy with hardware efficiency to address the computational and power demands of neural computations, especially in resource-constrained environments. The 11-bit (< 1-bit sign> < 6-bit exponent> < 4-bit mantissa>) CFP format extends the exponent range by a factor of 2 and achieves a dynamic range that is 15 times wider while preserving classification accuracy. An 11-bit CFP multiply-accumulate (MAC) unit is designed, and an FSM-controlled dot-product is implemented. Using this, the dense layer of an LSTM-based binary classification model was implemented. The design achieved 86.96% test accuracy with the IMDB dataset. The dot-product processor was synthesised using a TSMC 65 nm library and required a hardware area of 2029.60 µm2, consuming 0.374 mW at 500 MHz. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Electronics Letters is the property of Taylor & Francis Ltd 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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| Header | DbId: egs DbLabel: Engineering Source An: 193857781 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Hardware-efficient custom floating-point dot-product processor for LSTM. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Venkatesh%2C+Shreya%22">Venkatesh, Shreya</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sindhu%2C+R%22">Sindhu, R</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hallur%2C+Ashish+G%22">Hallur, Ashish G</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sharma%2C+Karra+Shivam%22">Sharma, Karra Shivam</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22V%2C+Arunachalam%22">V, Arunachalam</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> varunachalam@vit.ac.in</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Electronics+Letters%22">International Journal of Electronics Letters</searchLink>. Jun2026, Vol. 14 Issue 2, p242-254. 13p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Long+short-term+memory%22">Long short-term memory</searchLink><br /><searchLink fieldCode="DE" term="%22Floating-point+arithmetic%22">Floating-point arithmetic</searchLink><br /><searchLink fieldCode="DE" term="%22Recurrent+neural+networks%22">Recurrent neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Classification+algorithms%22">Classification algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Arithmetic%22">Arithmetic</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This paper presents a hardware-efficient implementation of dot-product operations tailored for Long Short-Term Memory (LSTM) Recurrent Neural Networks (RNNs) using custom floating-point (CFP) format. The proposed design introduces a precision-aware arithmetic scheme that balances accuracy with hardware efficiency to address the computational and power demands of neural computations, especially in resource-constrained environments. The 11-bit (< 1-bit sign> < 6-bit exponent> < 4-bit mantissa>) CFP format extends the exponent range by a factor of 2 and achieves a dynamic range that is 15 times wider while preserving classification accuracy. An 11-bit CFP multiply-accumulate (MAC) unit is designed, and an FSM-controlled dot-product is implemented. Using this, the dense layer of an LSTM-based binary classification model was implemented. The design achieved 86.96% test accuracy with the IMDB dataset. The dot-product processor was synthesised using a TSMC 65 nm library and required a hardware area of 2029.60 µm2, consuming 0.374 mW at 500 MHz. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Electronics Letters is the property of Taylor & Francis Ltd 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: 10.1080/21681724.2026.2620382 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 242 Subjects: – SubjectFull: Long short-term memory Type: general – SubjectFull: Floating-point arithmetic Type: general – SubjectFull: Recurrent neural networks Type: general – SubjectFull: Classification algorithms Type: general – SubjectFull: Arithmetic Type: general Titles: – TitleFull: Hardware-efficient custom floating-point dot-product processor for LSTM. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Venkatesh, Shreya – PersonEntity: Name: NameFull: Sindhu, R – PersonEntity: Name: NameFull: Hallur, Ashish G – PersonEntity: Name: NameFull: Sharma, Karra Shivam – PersonEntity: Name: NameFull: V, Arunachalam IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 21681724 Numbering: – Type: volume Value: 14 – Type: issue Value: 2 Titles: – TitleFull: International Journal of Electronics Letters Type: main |
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