• English
  • Deutsch
  • Log In
    Password Login
    Research Outputs
    Fundings & Projects
    Researchers
    Institutes
    Statistics
Repository logo
Fraunhofer-Gesellschaft
  1. Home
  2. Fraunhofer-Gesellschaft
  3. Scopus
  4. Adaptive Mixed MLC-SLC FeFET Mapping for CIM AI Applications Through Simulated Annealing
 
  • Details
  • Full
Options
2024
Conference Paper
Title

Adaptive Mixed MLC-SLC FeFET Mapping for CIM AI Applications Through Simulated Annealing

Abstract
This paper explores the novel use of ferroelectric field-effect transistors (FeFETs) in a mixed multi-level cell (MLC) and single-level cell (SLC) configuration, aiming to strike an optimal balance between area efficiency and data integrity. Addressing the inherent trade-offs in MLC configurations, which offer high bit capacity at the cost of increased error rates, we propose a mixed mapping scheme that effectively combines the advantages of both MLC and SLC configurations. Further, this study introduces a specialized fitting algorithm designed to identify the optimal configuration. Complemented by simulated annealing for hyperparameter tuning, this approach not only proves efficacious in this specific context but also offers adaptability for diverse design objectives.
Author(s)
Vardar, Alptekin
Fraunhofer-Institut für Photonische Mikrosysteme IPMS  
Müller, Franz  
Fraunhofer-Institut für Photonische Mikrosysteme IPMS  
Gecin, Ipek
Fraunhofer-Institut für Photonische Mikrosysteme IPMS  
Laleni, Nellie
Fraunhofer-Institut für Photonische Mikrosysteme IPMS  
Kämpfe, Thomas  orcid-logo
Fraunhofer-Institut für Photonische Mikrosysteme IPMS  
Mainwork
IEEE 6th International Conference on AI Circuits and Systems, AICAS 2024. Proceedings  
Conference
International Conference on AI Circuits and Systems 2024  
DOI
10.1109/AICAS59952.2024.10595943
Language
English
Fraunhofer-Institut für Photonische Mikrosysteme IPMS  
Keyword(s)
  • Edge Computing

  • FeFET

  • Hyperparameter Optimization

  • In-Memory Computing

  • Neural Networks

  • Non-Volatile Memory

  • Cookie settings
  • Imprint
  • Privacy policy
  • Api
  • Contact
© 2024