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Analog optical computer for AI inference and combinatorial optimization.

Kirill P Kalinin1, Jannes Gladrow2, Jiaqi Chu2

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An analog optical computer accelerates artificial intelligence (AI) and optimization tasks without energy-intensive digital conversions. This sustainable computing approach enhances efficiency and noise robustness for complex AI and optimization problems.

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Area of Science:

  • Computer Science
  • Optical Engineering
  • Artificial Intelligence

Background:

  • Digital computing's energy demands challenge AI and optimization sustainability.
  • Existing unconventional systems often require inefficient digital conversions and face hardware-software mismatches.
  • Analog noise is a significant challenge for current analog computing approaches.

Purpose of the Study:

  • To introduce a novel analog optical computer (AOC) for accelerating both AI inference and combinatorial optimization.
  • To demonstrate a dual-domain computing platform that overcomes limitations of existing systems.
  • To showcase a sustainable and efficient computing solution for demanding applications.

Main Methods:

  • Developed an analog optical computer integrating analog electronics and 3D optics.
  • Implemented a rapid fixed-point search to avoid digital conversions and improve noise robustness.
  • Co-designed hardware and a fixed-point abstraction for AI and optimization tasks.

Main Results:

  • The AOC accelerates AI inference and combinatorial optimization on a single platform.
  • Achieved enhanced noise robustness and efficiency by eliminating digital conversions.
  • Demonstrated capabilities in image classification, nonlinear regression, medical image reconstruction, and financial transaction settlement.

Conclusions:

  • The analog optical computer offers a promising path for faster and sustainable computing.
  • Native support for iterative, compute-intensive models enables a scalable analog platform for AI and optimization innovation.
  • Co-design of hardware and abstraction is key to advancing computing technologies.