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In-Memory-Computing Realization with a Photodiode/Memristor Based Vision Sensor.

Nikolaos Vasileiadis1,2, Vasileios Ntinas2, Georgios Ch Sirakoulis2

  • 1Institute of Nanoscience and Nanotechnology, National Center for Scientific Research "Demokritos", 15341 Agia Paraskevi, Greece.

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Summary
This summary is machine-generated.

This study introduces a novel photodiode vision sensor with in-memory computing, utilizing silicon nitride memristive devices for efficient edge computing in IoT applications.

Keywords:
IoTSPICEcrossbardot product engineedge computingin-memory computingmemristorphotodioderesistance switchingresistive random-access memory (RRAM)silicon nitridevision sensor

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Area of Science:

  • Materials Science
  • Electrical Engineering
  • Computer Science

Background:

  • Internet of Things (IoT) technologies demand portable, energy-efficient edge computing solutions.
  • Resistive switching (RS) devices, particularly resistive random-access memory (RRAM), offer potential for memory and in-memory computing.
  • Memristive properties are crucial for advanced computing architectures.

Purpose of the Study:

  • To present a new photodiode-based vision sensor architecture with integrated in-memory computing capabilities.
  • To leverage silicon nitride (SiNx) memristive devices for enhanced edge processing in vision sensors.
  • To demonstrate the feasibility of a photodiode-memristor pixel for efficient data processing.

Main Methods:

  • Characterization of resistance switching dynamics in a CMOS-compatible SiNx Metal-Insulator-Semiconductor (MIS) device.
  • Development of a data-fitted behavioral model for the memristive device.
  • SPICE simulations to evaluate the in-memory computing performance of the proposed photodiode-memristor pixel.

Main Results:

  • Successful demonstration of a memristive device with suitable resistance switching properties.
  • Extraction of a behavioral model accurately representing the device's dynamics.
  • SPICE simulations confirmed the in-memory computing potential of the photodiode-memristor vision sensor pixel.

Conclusions:

  • The proposed photodiode-memristor vision sensor architecture shows promise for in-memory computing at the edge.
  • This approach addresses the need for efficient processing in demanding IoT applications.
  • Further discussion on integration and manufacturing perspectives was provided.