View in EDS

Energy efficient memory architectures for next-generation wearable healthcare devices.

Saved in:
Bibliographic Details
Title: Energy efficient memory architectures for next-generation wearable healthcare devices.
Authors: Garg, Deepak1 (AUTHOR), Sharma, Devendra Kumar2 (AUTHOR) d_k_s1970@yahoo.co.in, Garg, Lalit2 (AUTHOR)
Source: Intelligent Decision Technologies. Nov2025, Vol. 19 Issue 6, p4297-4309. 13p.
Subjects: Wearable technology, Static random access memory, Energy conservation, Power resources management, Applied sciences, Energy consumption, Interdisciplinary research, Semiconductor technology
Abstract: The paper explores low-power design strategies for SRAM cells in wearable and implantable devices (WIDs) to address critical power limitations that hinder further miniaturization. FinFET solves the problem of leakage current (I_Leakage) by improving the challenging power versus performance trade-off. This research develops 7-Transistor SRAM cells based on FinFETs using the Multi Threshold CMOS (MTCMOS) and Upper Self Controllable Voltage Level (USVL) methods. Using 45 nm FinFET technologies, the design and simulation of all design circuits are carried out with Cadence Virtuoso. The work adopts a multi-disciplinary approach, combining device-circuit co-design to achieve ultra-low-power operations suitable for complex tasks in wearable and implantable micro systems. The proposed design shows that the USVL method of a 7T SRAM using FinFET is more effective than the MTCMOS methodology in terms of leakage power and leakage currents. Additionally, among other proposed approaches, a comparative analysis of leakage currents and leakage power is conducted. Key outcomes include significant improvements in leakage power through FinFET-based SRAM cell using USVL technique. This paper contributes to advancing low-leakage wearable/ implantable devices (WIDs) by integrating innovative leakage reduction techniques with cutting-edge low-power circuit designs. The proposed design achieves a minimum leakage current of 10.6 nA and leakage power of 26.98 nW by utilizing USVL approach. Compared to SRAM cells designed with the MTCMOS technique, the proposed method results in approximately 17.8% and 28% reduction in leakage power and leakage current, respectively. The findings pave the way for developing smaller, smarter, and sustainable wearable and implantable devices capable of complex tasks without reliance on batteries. [ABSTRACT FROM AUTHOR]
Copyright of Intelligent Decision Technologies is the property of Sage Publications Inc. 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
Header DbId: egs
DbLabel: Engineering Source
An: 189876596
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Energy efficient memory architectures for next-generation wearable healthcare devices.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Garg%2C+Deepak%22">Garg, Deepak</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sharma%2C+Devendra+Kumar%22">Sharma, Devendra Kumar</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> d_k_s1970@yahoo.co.in</i><br /><searchLink fieldCode="AR" term="%22Garg%2C+Lalit%22">Garg, Lalit</searchLink><relatesTo>2</relatesTo> (AUTHOR)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Intelligent+Decision+Technologies%22">Intelligent Decision Technologies</searchLink>. Nov2025, Vol. 19 Issue 6, p4297-4309. 13p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Wearable+technology%22">Wearable technology</searchLink><br /><searchLink fieldCode="DE" term="%22Static+random+access+memory%22">Static random access memory</searchLink><br /><searchLink fieldCode="DE" term="%22Energy+conservation%22">Energy conservation</searchLink><br /><searchLink fieldCode="DE" term="%22Power+resources+management%22">Power resources management</searchLink><br /><searchLink fieldCode="DE" term="%22Applied+sciences%22">Applied sciences</searchLink><br /><searchLink fieldCode="DE" term="%22Energy+consumption%22">Energy consumption</searchLink><br /><searchLink fieldCode="DE" term="%22Interdisciplinary+research%22">Interdisciplinary research</searchLink><br /><searchLink fieldCode="DE" term="%22Semiconductor+technology%22">Semiconductor technology</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The paper explores low-power design strategies for SRAM cells in wearable and implantable devices (WIDs) to address critical power limitations that hinder further miniaturization. FinFET solves the problem of leakage current (I_Leakage) by improving the challenging power versus performance trade-off. This research develops 7-Transistor SRAM cells based on FinFETs using the Multi Threshold CMOS (MTCMOS) and Upper Self Controllable Voltage Level (USVL) methods. Using 45 nm FinFET technologies, the design and simulation of all design circuits are carried out with Cadence Virtuoso. The work adopts a multi-disciplinary approach, combining device-circuit co-design to achieve ultra-low-power operations suitable for complex tasks in wearable and implantable micro systems. The proposed design shows that the USVL method of a 7T SRAM using FinFET is more effective than the MTCMOS methodology in terms of leakage power and leakage currents. Additionally, among other proposed approaches, a comparative analysis of leakage currents and leakage power is conducted. Key outcomes include significant improvements in leakage power through FinFET-based SRAM cell using USVL technique. This paper contributes to advancing low-leakage wearable/ implantable devices (WIDs) by integrating innovative leakage reduction techniques with cutting-edge low-power circuit designs. The proposed design achieves a minimum leakage current of 10.6 nA and leakage power of 26.98 nW by utilizing USVL approach. Compared to SRAM cells designed with the MTCMOS technique, the proposed method results in approximately 17.8% and 28% reduction in leakage power and leakage current, respectively. The findings pave the way for developing smaller, smarter, and sustainable wearable and implantable devices capable of complex tasks without reliance on batteries. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Intelligent Decision Technologies is the property of Sage Publications Inc. 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.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=189876596
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1177/18724981251370447
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 13
        StartPage: 4297
    Subjects:
      – SubjectFull: Wearable technology
        Type: general
      – SubjectFull: Static random access memory
        Type: general
      – SubjectFull: Energy conservation
        Type: general
      – SubjectFull: Power resources management
        Type: general
      – SubjectFull: Applied sciences
        Type: general
      – SubjectFull: Energy consumption
        Type: general
      – SubjectFull: Interdisciplinary research
        Type: general
      – SubjectFull: Semiconductor technology
        Type: general
    Titles:
      – TitleFull: Energy efficient memory architectures for next-generation wearable healthcare devices.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Garg, Deepak
      – PersonEntity:
          Name:
            NameFull: Sharma, Devendra Kumar
      – PersonEntity:
          Name:
            NameFull: Garg, Lalit
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 11
              Text: Nov2025
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-print
              Value: 18724981
          Numbering:
            – Type: volume
              Value: 19
            – Type: issue
              Value: 6
          Titles:
            – TitleFull: Intelligent Decision Technologies
              Type: main
ResultId 1