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Area of Science:

  • Photonics
  • Artificial Intelligence
  • Computer Engineering

Background:

  • Optical neural networks (ONNs) offer potential computing efficiency gains for artificial intelligence (AI).
  • Existing analog matrix-vector multiplication (MVM) in ONNs suffer from limited numerical precision due to noise in electro-optical processing.
  • This precision limitation hinders the performance of ONNs in complex AI tasks.

Purpose of the Study:

  • To propose and demonstrate a novel digital-analog hybrid MVM architecture for ONNs.
  • To achieve high numerical precision in optical computing without compromising efficiency.
  • To validate the practical application of this hybrid architecture in image processing and object detection.

Main Methods:

  • Fabrication of a proof-of-concept hybrid optical processor (HOP).
  • Testing the HOP for high-definition image processing, evaluating pixel error rate and signal-to-noise ratio.
  • Assessing accuracy in MNIST digit recognition tasks.
  • Applying the HOP to You Look Only Once (YOLO) object detection.

Main Results:

  • The fabricated HOP achieved 16-bit numerical precision.
  • Demonstrated a pixel error rate of 1.8 × 10-3 at an 18.2 dB signal-to-noise ratio in image processing.
  • Showcased no accuracy loss in MNIST digit recognition.
  • Confirmed the critical role of numerical precision for high-confidence object detection in YOLO.

Conclusions:

  • The digital-analog hybrid MVM architecture successfully enhances numerical precision in ONNs.
  • This approach overcomes the inherent limitations of purely analog optical computing.
  • The hybrid optical computing concept is applicable to various photonic MVM implementations, paving the way for accurate optical AI.