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Ultralow Power In-Sensor Neuronal Computing with Oscillatory Retinal Neurons for Frequency-Multiplexed, Parallel

Ragib Ahsan1, Hyun Uk Chae1, Seyedeh Atiyeh Abbasi Jalal1

  • 1Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California 90089, United States.

ACS Nano
|August 14, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces novel in-sensor computing using coupled oscillatory neuron devices for efficient image processing. This approach achieves significant power and speed advantages over traditional methods for tasks like edge detection and digit recognition.

Keywords:
in-sensor computingnegative differential resistanceoscillatoroscillatory retinal neuronsparallel computingultralow power computing

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Area of Science:

  • Neuromorphic Engineering
  • Integrated Photonics
  • Artificial Intelligence Hardware

Background:

  • Traditional computing architectures (von Neumann) face limitations in power and speed due to data movement and conversions.
  • Current in-sensor computing often relies on tunable sensors or weighting elements for linear operations.
  • Efficient, low-power processing at the sensor level is crucial for advanced AI and edge computing applications.

Purpose of the Study:

  • To implement in-sensor computing using oscillatory neuron devices for parallel, nonlinear operations.
  • To demonstrate image processing functions and neural network inference directly on a focal plane array.
  • To project the energy efficiency of this novel in-sensor computing approach.

Main Methods:

  • Utilized coupled oscillatory retinal neuron devices converting optical signals to voltage oscillations.
  • Developed a computing scheme based on frequency shifts of coupled oscillators for parallel, frequency-multiplexed nonlinear operations.
  • Experimentally implemented a 3x3 focal plane array for image processing tasks and MNIST digit recognition.

Main Results:

  • Demonstrated parallel execution of edge detection, thresholding, and segmentation functions on the 3x3 array.
  • Successfully performed experimental inference on handwritten digits from the MNIST database using the neuron array.
  • Projected ultra-low energy consumption for image processing operations, potentially as low as 15 aJ/OP.

Conclusions:

  • In-sensor computing with coupled oscillatory neurons enables efficient, parallel, nonlinear computations directly at the sensor.
  • This approach offers significant advantages in power and speed for image processing and AI inference.
  • The technology shows promise for next-generation low-power, high-performance edge computing systems.