Magnetic In/Near-Sensor Architectures: From Raw Sensing to Smart Processing

  • Sepehr Tabrizchi
  • , Ali Shafiee Sarvestani
  • , Md Hasibul Amin
  • , Deniz Najafi
  • , Shaahin Angizi
  • , Ramtin Zand
  • , Arman Roohi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Recent advances in spintronic devices and non-Von Neumann architectures present promising pathways to address the power and efficiency bottlenecks of conventional computing. Spintronic technologies - such as Magneto-Electric FETs and Spin-Orbit Torque MRAM - offer non-volatility, low-power operation, and high-speed performance, ideal for data- and compute-intensive workloads. Meanwhile, in-sensor processing enables energy-efficient, low-latency computing by tightly integrating sensing and computation, minimizing data movement. This survey reviews state-of-the-art magnetic-based integrated sensing and processing paradigms, outlining key capabilities, challenges in device integration, and emerging applications. We conclude with future research directions, underscoring the need for cross-disciplinary collaboration spanning device engineering, circuit design, and system architecture.

Original languageEnglish (US)
Title of host publicationGLSVLSI 2025 - Proceedings of the Great Lakes Symposium on VLSI 2025
PublisherAssociation for Computing Machinery
Pages593-599
Number of pages7
ISBN (Electronic)9798400714962
DOIs
StatePublished - Jun 29 2025
Event35th Edition of the Great Lakes Symposium on VLSI 2025, GLSVLSI 2025 - New Orleans, United States
Duration: Jun 30 2025Jul 2 2025

Publication series

NameProceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI

Conference

Conference35th Edition of the Great Lakes Symposium on VLSI 2025, GLSVLSI 2025
Country/TerritoryUnited States
CityNew Orleans
Period6/30/257/2/25

All Science Journal Classification (ASJC) codes

  • General Engineering

Fingerprint

Dive into the research topics of 'Magnetic In/Near-Sensor Architectures: From Raw Sensing to Smart Processing'. Together they form a unique fingerprint.

Cite this