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A Photonic System for Generating Unconditional Polarization-Entangled Photons Based on Multiple Quantum Interference

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Photonic online learning: a perspective.

Sonia Mary Buckley¹, Alexander N Tait², Adam N McCaughan¹

¹Applied Physics Division, National Institute of Standards and Technology, Boulder, CO 80305, USA.

Nanophotonics (Berlin, Germany)

|March 13, 2023

Summary

This summary is machine-generated.

Photonic neuromorphic hardware offers efficient computing but requires intensive training. Integrating online learning directly into the hardware is crucial for unlocking its full potential in neural networks.

Keywords:

integrated photonics neural networks neuromorphic photonics

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Area of Science:

Neuromorphic computing
Photonic integrated circuits
Artificial intelligence hardware

Background:

Neuromorphic hardware leverages physical processes for neural network computation, promising speed and energy efficiency.
Photonic neuromorphic hardware shows promise but faces challenges with power-hungry and time-consuming training methods.

Purpose of the Study:

To investigate the online learning paradigm for programming neuromorphic hardware.
To argue for the necessity of integrated online learning in photonic neuromorphic systems.

Main Methods:

Examination of the online learning paradigm in the context of neuromorphic hardware.
Analysis of the requirements for efficient training of photonic neural networks.

Main Results:

Online learning, where training is integrated into the hardware, is explored as a solution to current training limitations.
The study posits that online learning is essential for the practical realization of photonic neuromorphic hardware's capabilities.

Conclusions:

Online learning is a critical enabler for overcoming the training bottlenecks in photonic neuromorphic hardware.
Integrating online learning mechanisms is vital for achieving the full potential of energy-efficient, high-speed neuromorphic computing.