COMPREHENSIVE COMPARISON OF PHOTONIC AND TRADITIONAL ELECTRONIC CPUS
35 31
Keywords:
Photonic CPUs, Electronic CPUs, High-performance computing, Energy efficiency, Optical processors, Wavelength division multiplexing (WDM), Hybrid architectures, Machine learning.Abstract
As computing technology progresses, the comparison between photonic CPUs and traditional electronic CPUs has become a key topic in research and development. This paper offers a comprehensive analysis of the underlying principles, performance characteristics, and challenges associated with both processor types. Traditional electronic CPUs, which rely on the manipulation of electrons through silicon transistors, face inherent physical limitations, particularly in heat management, power consumption, and signal loss as they scale. Photonic CPUs, by contrast, use photons for data transmission, providing significant advantages in speed, bandwidth, and energy efficiency, particularly in large-scale data-intensive applications. However, despite their promising benefits, photonic CPUs are still in the early stages of development and face challenges related to serial task execution and compatibility with existing electronic systems. This paper explores the potential for hybrid architectures that combine the strengths of both photonic and electronic processors, which may offer a path toward future high-performance computing. Such an approach could be particularly impactful in fields like artificial intelligence, big data, and optical networking. The paper further reviews current research, explores potential applications, and discusses future prospects for these technologies, emphasizing the need for continued innovation to fully unlock the potential of photonic processing.
References
Yu, S., Liu, W., Tao, SJ. et al. A von-Neumann-like photonic processor and its application in studying quantum signature of chaos. Light Sci Appl 13, 74 (2024). https://doi.org/10.1038/s41377-024-01413-5
Shastri, B.J., Tait, A.N., Ferreira de Lima, T. et al. Photonics for artificial intelligence and neuromorphic computing. Nat. Photonics 15, 102–114 (2021). https://doi.org/10.1038/s41566-020-00754-y
Miller, D. A. B. (2020). Attojoule Optoelectronics for Low-Energy Information Processing and Communications. Journal of Lightwave Technology, 35(3), 346–396. https://doi.org/10.1109/JLT.2016.2630022
Shastri, B. J., Tait, A. N., Ferreira de Lima, T., Pernice, W. H., Bhaskaran, H., Wright, C. D., & Prucnal, P. R. (2020). Photonics for artificial intelligence and neuromorphic computing. Nature Photonics, 15(2), 102–114. https://doi.org/10.1038/s41566-020-00754-y
Sun, C., Wade, M. T., Lee, Y., Orcutt, J. S., Alloatti, L., Georgas, M. S., ... & Stojanović, V. (2021). Single-chip microprocessor that communicates directly using light. Nature, 528(7583), 534–538. https://doi.org/10.1038/nature16454
Rudolph, J., & Miller, D. A. B. (2022). Energy Efficiency of Photonic and Electronic Computation. IEEE Journal of Selected Topics in Quantum Electronics, 22(6), 1–12. https://doi.org/10.1109/JSTQE.2016.2587722
Hecht, J. (2020). Silicon photonics reaches a turning point. Optics and Photonics News, 30(1), 24–31. https://doi.org/10.1364/OPN.30.1.000024
Markov, I. L. (2021). Limits on fundamental limits to computation. Nature, 512(7513), 147–154. https://doi.org/10.1038/nature13570
Bogaerts, W., Pérez, D., Capmany, J., Miller, D. A. B., Poon, J., Englund, D., Morichetti, F., & Melloni, A. (2020). Programmable photonic circuits. Nature, 586(7828), 207–216. https://doi.org/10.1038/s41586-020-2764-0
Tait, A. N., Nahmias, M. A., Shastri, B. J., & Prucnal, P. R. (2021). Broadcast and Weight: An Integrated Network for Scalable Photonic Spike Processing. IEEE Journal of Selected Topics in Quantum Electronics, 22(6), 294–306. https://doi.org/10.1109/JSTQE.2015.2512760
Shen, Y., Harris, N. C., Skirlo, S., Prabhu, M., Baehr-Jones, T., Hochberg, M., Sun, X., & Soljačić, M. (2022). Deep learning with coherent nanophotonic circuits. Nature Photonics, 11(7), 441–446. https://doi.org/10.1038/nphoton.2017.93
Chakraborty, S., & Yin, X. (2023). The future of AI hardware: challenges and opportunities for photonic and quantum processors. Journal of High-Performance Computing, 35(4), 509–524. https://doi.org/10.1016/j.jhpc.2023.03.005
Hosseini, E. S., Poon, J. K. S., & Miller, D. A. B. (2021). Towards hybrid photonic-electronic computing. Journal of Applied Physics, 130(4), 041101. https://doi.org/10.1063/5.0061394
Wetzstein, G. (2020). Augmented and Virtual Reality. In: Murmann, B., Hoefflinger, B. (eds) NANO-CHIPS 2030. The Frontiers Collection. Springer, Cham. https://doi.org/10.1007/978-3-030-18338-7_25
