Contribution to a conference proceedings FZJ-2024-04731

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png
Analog Feedback-Controlled Memristor Programming Circuit for Analog Content Addressable Memory

 ;  ;  ;  ;  ;

2023
IEEE

2023 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE), MilanoMilano, Italy, 25 Oct 2023 - 27 Oct 20232023-10-252023-10-27 IEEE 6, 983-988 () [10.1109/MetroXRAINE58569.2023.10405732]

This record in other databases:

Please use a persistent id in citations: doi:  doi:

Abstract: Recent breakthroughs in associative memories suggest that silicon memories are coming closer to human memories, especially for memristive Content Addressable Memories (CAMs) which are capable to read and write in analog values. However, the Program-Verify algorithm, the state-of-the-art memristor programming algorithm, requires frequent switching between verifying and programming memristor conductance, which brings many defects such as high dynamic power and long programming time. Here, we propose an analog feedback-controlled memristor programming circuit that makes use of a novel look-up table-based (LUT-based) programming algorithm. With the proposed algorithm, the programming and the verification of a memristor can be performed in a single-direction sequential process. Besides, we also integrated a single proposed programming circuit with eight analog CAM (aCAM) cells to build an aCAM array. We present SPICE simulations on TSMC 28nm process. The theoretical analysis shows that 1. A memristor conductance within an aCAM cell can be converted to an output boundary voltage in aCAM searching operations and 2. An output boundary voltage in aCAM searching operations can be converted to a programming data line voltage in aCAM programming operations. The simulation results of the proposed programming circuit prove the theoretical analysis and thus verify the feasibility to program memristors without frequently switching between verifying and programming the conductance. Besides, the simulation results of the proposed aCAM array show that the proposed programming circuit can be integrated into a large array architecture.


Contributing Institute(s):
  1. Neuromorphic Compute Nodes (PGI-14)
Research Program(s):
  1. 5234 - Emerging NC Architectures (POF4-523) (POF4-523)
  2. 5233 - Memristive Materials and Devices (POF4-523) (POF4-523)

Appears in the scientific report 2024
Database coverage:
OpenAccess
Click to display QR Code for this record

The record appears in these collections:
Document types > Events > Contributions to a conference proceedings
Institute Collections > PGI > PGI-14
Workflow collections > Public records
Publications database
Open Access

 Record created 2024-07-05, last modified 2025-02-20


OpenAccess:
Download fulltext PDF
External link:
Download fulltextFulltext
Rate this document:

Rate this document:
1
2
3
 
(Not yet reviewed)