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Optoelectronic Devices for In-Sensor Computing.

Qinqi Ren1,2, Chaoyi Zhu1,2, Sijie Ma1,2

  • 1Department of Applied Physics, The Hong Kong Polytechnic University, Kowloon, Hong Kong, 999077, China.

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|July 15, 2024

View abstract on PubMed

Summary
This summary is machine-generated.

In-sensor computing integrates processing within sensors to manage massive data from increasing sensory nodes. This approach enhances efficiency by compressing and structuring data at the source, addressing challenges in power, bandwidth, and latency.

Keywords:
data compressiondata structuringin‐sensor computingoptoelectronic devicesswitching mechanisms

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Materials Science

Background:

  • Increasing sensory nodes generate massive, unstructured data, posing challenges for transmission, storage, and processing.
  • Efficient data compression and structuring at sensory terminals are crucial for managing this data deluge.
  • In-sensor computing integrates perception, memory, and processing within sensors to address these challenges.

Purpose of the Study:

  • To explore the functions of electronic, optical, and optoelectronic hardware for in-sensor visual processing.
  • To examine hardware implementations of optoelectronic devices for compressing and structuring multidimensional vision information.
  • To investigate resistive switching mechanisms in optoelectronic devices for in-sensor computing operations.

Main Methods:

  • Adoption of vision sensors as a case study for in-sensor visual processing.
  • Examination of hardware implementations of optoelectronic devices for data compression and structuring.
  • Exploration of resistive switching mechanisms in volatile and nonvolatile optoelectronic devices.

Main Results:

  • Demonstration of optoelectronic devices' capability to perform in-sensor visual processing, including data compression and structuring.
  • Analysis of resistive switching mechanisms underlying the operation of these devices.
  • Identification of potential for efficient processing of multidimensional vision data.

Conclusions:

  • In-sensor computing, particularly using optoelectronic devices, offers a promising solution for efficient processing of massive sensory data.
  • Hardware implementations of optoelectronic devices can effectively compress and structure visual information at the sensor level.
  • Future development of optoelectronic devices holds significant potential for advancing in-sensor computing capabilities.