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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.
Materials (Basel, Switzerland)
|September 28, 2021
Summary
This study introduces a novel photodiode vision sensor with in-memory computing, utilizing silicon nitride memristive devices for efficient edge computing in IoT applications.
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.
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