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A Reconfigurable Memristor-Based Computing-in-Memory Circuit for Content-Addressable Memory in Sensor Systems.

Hao Hu1, Yian Liu1,2, Shuang Liu1

  • 1State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu 610054, China.

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This study introduces a novel memristor-based circuit for energy-efficient edge computing. It enhances Content-Addressable Memory (CAM) performance for sensor networks, enabling faster data processing and reduced power consumption.

Keywords:
approximate matchingcomputing-in-memorycontent-addressable memorymemristormulti-bit processing

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Area of Science:

  • Materials Science
  • Computer Engineering
  • Electrical Engineering

Background:

  • Growing demand for energy-efficient, high-performance computing in resource-limited sensor edge applications.
  • Limitations of traditional binary computing in storage density and computational efficiency.

Purpose of the Study:

  • To present a reconfigurable memristor-based computing-in-memory circuit for Content-Addressable Memory (CAM).
  • To enhance functional flexibility by enabling dynamic switching between exact and approximate CAM modes.
  • To provide a hardware solution for intelligent edge computing in next-generation sensor networks.

Main Methods:

  • Exploiting analog multi-level resistance characteristics of memristors for parallel multi-bit processing.
  • Employing dynamic adjustment of input signal to reference voltage mapping.
  • Utilizing a TiN/TiOx/HfO2/TiN memristor array structure (32x36).

Main Results:

  • Demonstrated eight stable and distinguishable resistance states with excellent retention.
  • Achieved a minimum voltage separation exceeding 6.5 mV between state-representing waveforms in large-scale simulations, ensuring reliable readout.
  • Showcased significant improvements in storage density and computational efficiency.

Conclusions:

  • The developed circuit offers an efficient and scalable hardware solution for intelligent edge computing.
  • Suitable for applications like real-time biometric recognition, sensor data fusion, and AI inference.
  • Effectively reduces system dependence on cloud communication and lowers overall power consumption.