The human brain processes information through synapses and neurons. It performs complex tasks with high efficiency while using minimal energy. Neuromorphic computing mimics this biological system, using artificial neurons and synapses to enhance computational speed and reduce power consumption.
As computing demands grow, photonics is becoming an essential component in neuromorphic architectures. But how is light being integrated into these systems, and what advantages does it offer over traditional electronics?
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What is a Photonic Neuromorphic Computing System?
A photonic neuromorphic computing system is a computational framework that uses light to process information, rather than electricity.
Photonic systems manipulate light through waveguides, modulators, and integrated circuits, enabling parallel processing and ultra-fast signal transmission. Photonic systems are faster and more energy-efficient than traditional electronic computers, as they don't suffer from electrical resistance and heat generation.
These benefits make photonic neuromorphic computing a promising solution for artificial intelligence (AI), deep learning, and real-time data processing.
Neuromorphic Computing Is a Big Deal for A.I., But What Is It?
How Do Photonic Neuromorphic Computing Systems Work?
Neuromorphic computing systems rely on Photonic Integrated Circuits (PICs). These circuits consist of optical neurons, modulators, memories, and photodetectors, all compactly integrated onto a single chip. Unlike electronic circuits, PICs use photons instead of electrons, allowing for higher-speed computation with minimal energy loss.
Light propagates through these circuits at nearly the speed of light, transmitting and processing information with sub-nanosecond latency. A key advantage of PICs is their ability to operate in parallel, meaning multiple signals can travel independently through different wavelengths and polarizations without interference.
Recent research has demonstrated that photonic neuromorphic processors can achieve 16 peta operations per second (POPS/s) while consuming less than 2 watts of power. This performance far surpasses traditional computing hardware.
Applications of Photonic Neuromorphic Computing
Optical Communication
Photonic neuromorphic computing is playing an increasing role in optical communication systems. Beyond signal transmission and channel equalization, these systems enhance optical header recognition and data recovery, enabling faster and more efficient processing of high-bandwidth optical signals.
AI Systems and Deep Learning
The increasing complexity of AI and deep learning requires hardware capable of processing large datasets efficiently. Neuromorphic computing is helping accelerate AI workloads. These systems achieve high computational rates per neuron synapse and optimize NxN weight matrix deployments, making them well-suited for deep learning applications.
In 2023, researchers from Greece and California-based startup Celestial AI developed a neuromorphic silicon photonics chip designed to enhance AI frameworks. Their approach introduced a hybrid architecture that combined a linear optical framework with SiGe electro-absorption modulator (EAM) technology to improve performance.
This photonic Xbar architecture reduced power losses and improved matrix-vector multiplication accuracy. Tests showed that an EAM-based silicon photonic neuromorphic chip built on Xbar technology supported 4-bit resolution artificial neural networks (ANNs). It achieved a 50 GHz computational rate with over 95 % accuracy.
Ultra-Fast Image Processing
Neuromorphic computing systems, like classical ANNs, are highly efficient in image classification and pattern recognition.
Spiking Neural Networks (SNNs) have demonstrated exceptional image processing capabilities. These systems use convolution and pooling techniques for feature extraction. They can be integrated with photon avalanche detectors and dynamic vision sensor (DVS) cameras to process images much faster than conventional methods.
Vertical Cavity Surface Emitting Lasers (VCSELs) are a type of semiconductor laser that operates with low energy and integrates easily into modern technology. Researchers have explored the potential of VCSEL neurons for image processing by combining photonic neuromorphic computing with VCSEL-based systems. This led to the development of an all-optical spiking platform designed for high-speed image processing.
The new platform uses a single VCSEL as an artificial optical spiking neuron. It is integrated with a multiplexing mechanism to improve system efficiency. Researchers tested the platform in neuromorphic edge-feature detection experiments, where streams of optical input pulses were processed using a single VCSEL. The system successfully applied consecutive 2×2 kernel operators to images, enabling fast neuromorphic spiking events for edge detection.
In testing, the platform processed 5,000 images from the MNIST handwritten digit database in a single experimental run. It handled 500 images per digit within 6.56 milliseconds, achieving a mean classification accuracy of 96.1 %. The system used commercially available devices and telecom-wavelength components, requiring no specialized VCSEL optimization.
These results demonstrate the potential of photonic neuromorphic computing for fast, energy-efficient, and hardware-friendly image processing. VCSEL-based spiking neurons could lead to high-speed, telecom-compatible neuromorphic platforms for real-time vision applications.9
Challenges in the Adoption of Photonic Neuromorphic Computing
Despite its advantages, photonic neuromorphic computing faces several challenges. Current PICs require significant energy to maintain optical stability. This challenge becomes even more pressing for portable and large-scale systems, where energy efficiency is critical.
Researchers are investigating high-mobility materials like Indium Gallium Arsenide (InGaAs) to develop more efficient photonic platforms with lower power requirements.
Material limitations are also a concern. No single material possesses all the properties needed for an ideal photonic neuromorphic circuit. To overcome this, hybrid approaches are being explored, integrating phase-change materials and modulators to enhance computational flexibility and non-linearity. These advancements aim to improve signal processing efficiency and operational reliability.
To improve scalability, researchers are exploring meta-surfaces, which are being tested for their ability to create compact, high-performance photonic devices. These advancements could enable widespread industrial adoption in the near future.
Another approach involves integrating Complementary Metal-Oxide-Semiconductor (CMOS) technology with silicon photonics, enhancing tuning modulation, stability, and overall system efficiency.
Sustainability is also becoming a key focus, as regulations on eco-friendly material sourcing become stricter. The development of environmentally sustainable photonic components will be essential for long-term adoption. At the same time, the rise of photonic neuromorphic computing introduces privacy and security concerns, as its high processing speed and connectivity introduce potential data vulnerabilities.
Despite these challenges, research is advancing to make photonic neuromorphic systems more efficient, scalable, and ready for real-world applications. With improvements in materials, power efficiency, and integration, these technologies could reshape AI, computing, and next-generation digital systems.
To learn more about the latest advancements in photonic computing, AI hardware, and semiconductor technology, please visit:
References and Further Reading
- Marković, D.. et al. (2020). Physics for neuromorphic computing. Nat Rev Phys. https://doi.org/10.1038/s42254-020-0208-2
- Chen, C., et al. (2019). Custom Sub-Systems and Circuits for Deep Learning: Guest Editorial Overview. IEEE Journal on Emerging and Selected Topics in Circuits and Systems. https://www.doi.org/10.1109/JETCAS.2019.2918317
- Kutluyarov R., et al. (2023). Neuromorphic Photonics Circuits: Contemporary Review. Nanomaterials. https://doi.org/10.3390/nano13243139
- Xu, R., et al. (2023). Hybrid photonic integrated circuits for neuromorphic computing. Optical Materials Express. https://doi.org/10.1364/OME.502179
- Argyris, A. (2022). Photonic neuromorphic technologies in optical communications. Nanophotonics. https://doi.org/10.1515/nanoph-2021-0578
- Moralis-Pegios, M., et al. (2022). Neuromorphic silicon photonics and hardware-aware deep learning for high-speed inference. Journal of Lightwave Technology. https://doi.org/10.1109/JLT.2022.3171831
- Giamougiannis, G., et al. (2022). Universal linear optics revisited: new perspectives for neuromorphic computing with silicon photonics. IEEE Journal of Selected Topics in Quantum Electronics. https://doi.org/10.1109/JSTQE.2022.3228318
- Kujan, B. (2023). Light meets Deep Learning: Computing fast enough for Next-Gen AI. [Online] IEEE Photonics. Available at: https://ieeephotonics.org/announcements/light-meets-deep-learning-computing-fast-enough-for-next-gen-ai/ [Accessed on: February 08, 2025].
- Robertson, J., et al. (2022). Ultrafast neuromorphic photonic image processing with a VCSEL neuron. Sci Rep. https://doi.org/10.1038/s41598-022-08703-1
- Li, R., et. al. (2025). Photonics for Neuromorphic Computing: Fundamentals, Devices, and Opportunities. Advanced Materials. https://doi.org/10.1002/adma.202312825
- Shastri, B., et. al. (2022). Silicon photonics for neuromorphic computing and artificial intelligence: applications and roadmap. 2022 Photonics & Electromagnetics Research Symposium (PIERS). https://doi.org/10.1109/PIERS55526.2022.9792850
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