AI and Integrated Photonics for Energy-Efficient Computing: Emerging Solutions to the Computational Demand of Modern AI

Allen Torbert

AI and Integrated Photonics for Energy-Efficient Computing: Emerging Solutions to the Computational Demand of Modern AI

Author(s)	Allen Torbert
Country	United States
Abstract	The rapid expansion of artificial intelligence is creating unprecedented demand for computational capacity, memory bandwidth and data movement, resulting in substantial energy consumption across data centres, high-performance computing facilities and edge devices. Integrated photonics has emerged as a promising technological pathway for addressing these challenges by exploiting optical signals for high-bandwidth and potentially energy-efficient computation and communication. This paper examines the convergence of artificial intelligence and integrated photonics as an emerging approach to sustainable computing. It explores photonic computing architectures, silicon photonics, optical neural networks, photonic tensor processing, wavelength-division multiplexing, optical interconnects and photonic accelerators for machine-learning workloads. Particular attention is given to the potential of photonic technologies to accelerate matrix multiplication, convolution, signal processing and neural-network inference while reducing the energy and latency associated with electrical data movement. The paper proposes a conceptual AI–photonics computing framework integrating electronic processors, photonic accelerators, optical interconnects, high-bandwidth memory and intelligent workload scheduling. Applications in large language models, computer vision, scientific computing, data-centre acceleration and edge AI are discussed. The study also examines major limitations, including optical-device losses, analogue precision, thermal sensitivity, electro-optical conversion overhead, memory integration, manufacturing variability and software–hardware co-design challenges. The analysis suggests that the most realistic near-term opportunity lies in heterogeneous computing architectures where photonic and electronic processors perform complementary tasks rather than in fully optical computers. Future advances in photonic integration, optical memory, nonlinear photonic devices, packaging, AI-aware compilers and manufacturing processes could enable highly energy-efficient computing platforms capable of meeting the growing computational requirements of modern artificial intelligence.
Keywords	Integrated Photonics, Photonic Computing, Artificial Intelligence, Optical Neural Networks, Silicon Photonics, Energy-Efficient Computing, AI Accelerators, Photonic Processors, Optical Interconnects, Sustainable Computing.
Field	Engineering
Published In	Volume 7, Issue 3, May-June 2025
Published On	2025-06-22

View / Download PDF File

About AJAMRI Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+1-872-302-3851	editor@ajamri.com

American Journal of Advanced Multidisciplinary Research and Innovation

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

AI and Integrated Photonics for Energy-Efficient Computing: Emerging Solutions to the Computational Demand of Modern AI

Share this