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Integrated Neuromorphic Photonic Computing for AI Acceleration: Emerging Devices, Network Architectures, and Future

Gaofei Wang1,2,3, Junyan Che1,2,3, Chen Gao1,2,3

  • 1College of Integrated Circuits & Micro-Nano Electronics, Fudan University, 220 Handan Road, Shanghai, 200433, P. R. China.

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This summary is machine-generated.

Photonic neuromorphic computing offers a solution to AI hardware limitations by using light for faster, more energy-efficient computations. This review details progress in photonic neural networks (PNNs) for next-generation AI acceleration.

Keywords:
AI acceleratorintegrated photonicsneuromorphic photonicsphotonic computingphotonic neural networks

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Area of Science:

  • Photonic Neuromorphic Computing
  • Artificial Intelligence Hardware Acceleration
  • Integrated Photonics

Background:

  • Electronic hardware faces physical limits (transistor scaling, von Neumann architecture, thermal dissipation) hindering AI computational density and energy efficiency.
  • Deep learning, including large language models (LLMs), demands significant computational resources, exacerbating hardware bottlenecks.

Purpose of the Study:

  • To review a decade of progress in photonic neural networks (PNNs) as a solution for AI hardware limitations.
  • To analyze core PNN components, network architectures, and application-specific requirements for cloud and edge AI.
  • To outline pathways for overcoming material and system-level barriers in PNN development.

Main Methods:

  • Systematic review and critical analysis of advances in linear synaptic devices, nonlinear neuron devices, and PNN architectures.
  • Analysis of application-specific requirements for PNN deployment in cloud-scale and edge/client-side AI.
  • Identification of material and system-level barriers and proposed solutions, including topology-optimized devices and advanced packaging.

Main Results:

  • Photonic neural networks (PNNs) demonstrate potential for AI acceleration, achieving single-chip integration of inference and in situ training.
  • Significant progress has been made in core PNN components, though challenges remain.
  • PNNs leverage light's parallelism, low latency, and minimal thermal loss for efficient matrix operations.

Conclusions:

  • Photonic neuromorphic computing, particularly PNNs, presents a paradigm shift for post-Moore AI hardware.
  • Overcoming material and packaging challenges is crucial for widespread PNN deployment.
  • PNNs offer a promising platform for next-generation, energy-efficient AI acceleration.