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Area of Science:

  • Optoelectronics and Photonics
  • Materials Science
  • Computer Engineering

Background:

  • The von Neumann bottleneck limits computational efficiency in traditional architectures.
  • Hybrid photonic-electronic systems offer a potential solution but face implementation challenges.
  • Phase-change materials (PCMs) are promising for non-volatile memory and computing.

Purpose of the Study:

  • To demonstrate a successful in-memory photonic-electronic dot-product engine.
  • To decouple electronic programming from photonic computation for improved performance.
  • To achieve high-accuracy image processing using hybrid computing.

Main Methods:

  • Development of non-volatile, electronically reprogrammable PCM memory cells.
  • Utilized non-resonant silicon-on-insulator waveguide microheater devices.
  • Implemented parallel multiplications for image processing tasks.

Main Results:

  • Achieved record 4-bit weight encoding in PCM memory cells.
  • Demonstrated lowest energy consumption (1.7 nJ/dB) for Erase operation.
  • Obtained high switching contrast (158.5%) and superior image processing accuracy (SNR ≥87.36, σ ≤0.007).

Conclusions:

  • Successfully demonstrated an in-memory hybrid computing system for convolutional processing.
  • Achieved 86% and 87% inferencing accuracies on the MNIST database.
  • This work represents a milestone in hybrid photonic-electronic computing, resolving the von Neumann bottleneck.