Feature Overlapping: Temporal Differential Decoupling for Efficient Spiking Neural Network Training.
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| Title: | Feature Overlapping: Temporal Differential Decoupling for Efficient Spiking Neural Network Training. |
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| Authors: | Liu, Yuqian1 (AUTHOR), Wang, Yuechao1 (AUTHOR), Jiang, Yizhou1 (AUTHOR), Chen, Guanyu1 (AUTHOR), Xu, Tianrun1 (AUTHOR), Chen, Feng1 (AUTHOR) chenfeng@mail.tsinghua.edu.cn |
| Source: | Annals of the New York Academy of Sciences. Feb2026, Vol. 1556 Issue 1, p1-13. 13p. |
| Subjects: | Power aware computing, Sensitivity analysis, Artificial neural networks, Approximation error |
| Abstract: | Spiking neural networks (SNNs) possess great potential for energy‐efficient computation; however, their practical deployment is often constrained by the high computational cost associated with training across multiple time steps. Unlike previous approaches that simply reduce the number of time steps, this work investigates temporal feature redundancy and reveals the feature overlapping phenomenon in SNNs—showing that substantial computational redundancy exists across temporal dimensions. To address this issue, we propose our core contribution, temporal differential decoupling (TDD), which transforms network computation into the differential domain to disentangle static and dynamic feature components. This decoupling enables focused processing of informative signals and significantly reduces redundant computation without compromising model accuracy. Building upon this framework, we further design the TDD‐based differential domain low‐sparsity approximation (TDD‐DDLA) algorithm, which is based on the gradient sensitivity criterion, as an implementation strategy to quantify each temporal feature's contribution to gradient updates and achieve efficient energy optimization. In contrast to prior studies that only adjusted time steps without explicit feature‐level analysis, our framework provides a structured and theoretically grounded analysis of temporal feature evolution. Experimental results demonstrate that our method achieves up to 80.9% fewer spikes per time step and 57.8% fewer total spikes, with no degradation in classification performance, offering a promising pathway toward scalable, low‐cost, and high‐accuracy SNN deployment. [ABSTRACT FROM AUTHOR] |
| Copyright of Annals of the New York Academy of Sciences is the property of Wiley-Blackwell 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 |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 192094172 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Feature Overlapping: Temporal Differential Decoupling for Efficient Spiking Neural Network Training. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Liu%2C+Yuqian%22">Liu, Yuqian</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Yuechao%22">Wang, Yuechao</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jiang%2C+Yizhou%22">Jiang, Yizhou</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chen%2C+Guanyu%22">Chen, Guanyu</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xu%2C+Tianrun%22">Xu, Tianrun</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chen%2C+Feng%22">Chen, Feng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> chenfeng@mail.tsinghua.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Annals+of+the+New+York+Academy+of+Sciences%22">Annals of the New York Academy of Sciences</searchLink>. Feb2026, Vol. 1556 Issue 1, p1-13. 13p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Power+aware+computing%22">Power aware computing</searchLink><br /><searchLink fieldCode="DE" term="%22Sensitivity+analysis%22">Sensitivity analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Approximation+error%22">Approximation error</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Spiking neural networks (SNNs) possess great potential for energy‐efficient computation; however, their practical deployment is often constrained by the high computational cost associated with training across multiple time steps. Unlike previous approaches that simply reduce the number of time steps, this work investigates temporal feature redundancy and reveals the feature overlapping phenomenon in SNNs—showing that substantial computational redundancy exists across temporal dimensions. To address this issue, we propose our core contribution, temporal differential decoupling (TDD), which transforms network computation into the differential domain to disentangle static and dynamic feature components. This decoupling enables focused processing of informative signals and significantly reduces redundant computation without compromising model accuracy. Building upon this framework, we further design the TDD‐based differential domain low‐sparsity approximation (TDD‐DDLA) algorithm, which is based on the gradient sensitivity criterion, as an implementation strategy to quantify each temporal feature's contribution to gradient updates and achieve efficient energy optimization. In contrast to prior studies that only adjusted time steps without explicit feature‐level analysis, our framework provides a structured and theoretically grounded analysis of temporal feature evolution. Experimental results demonstrate that our method achieves up to 80.9% fewer spikes per time step and 57.8% fewer total spikes, with no degradation in classification performance, offering a promising pathway toward scalable, low‐cost, and high‐accuracy SNN deployment. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Annals of the New York Academy of Sciences is the property of Wiley-Blackwell 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: 10.1111/nyas.70204 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 1 Subjects: – SubjectFull: Power aware computing Type: general – SubjectFull: Sensitivity analysis Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: Approximation error Type: general Titles: – TitleFull: Feature Overlapping: Temporal Differential Decoupling for Efficient Spiking Neural Network Training. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Liu, Yuqian – PersonEntity: Name: NameFull: Wang, Yuechao – PersonEntity: Name: NameFull: Jiang, Yizhou – PersonEntity: Name: NameFull: Chen, Guanyu – PersonEntity: Name: NameFull: Xu, Tianrun – PersonEntity: Name: NameFull: Chen, Feng IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Text: Feb2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 00778923 Numbering: – Type: volume Value: 1556 – Type: issue Value: 1 Titles: – TitleFull: Annals of the New York Academy of Sciences Type: main |
| ResultId | 1 |
