Sean's Blog

Photonic Computing

Photonic computing uses light (photons) instead of electrons to perform calculations, offering faster processing speeds and lower energy consumption than traditional electronic processors. Since photons travel at the speed of light and generate minimal heat, photonic systems can perform massive parallel operations with reduced thermal management requirements - making photonic computing a very promising development.

How Photonic Computing Works

Photonic computers manipulate light signals using various optical components:

Optical Logic Gates: Instead of transistors switching electrical signals, photonic systems use devices like Mach-Zehnder interferometers, ring resonators or nonlinear optical materials to perform logical operations on light beams. These can implement AND, OR, NOT and other fundamental operations of computing.

Wavelength Division Multiplexing: Multiple data streams can be encoded on different wavelengths of light traveling through the same optical waveguide simultaneously, enabling massive parallelism that's difficult to achieve with electrons.

Photonic Integrated Circuits: Similar to electronic chips, these integrate lasers, modulators, waveguides and photodetectors on single substrates, typically made from silicon, indium phosphid or lithium niobate.

From Lab to Market

Photonic computing transitioned from laboratory experiments to commercial systems in 2025. Multiple systems now deliver practical AI acceleration with performance that matches or exceeds traditional electronic alternatives while consuming 30 to 1,000 times less energy.

Key Breakthroughs

MIT's Integrated Processor: In December 2024, MIT researchers demonstrated the first fully integrated photonic processor capable of performing all key deep neural network computations optically on a single chip. The system achieves classification tasks in less than 0.5 nanoseconds with over 92% accuracy.

Lightmatter's Achievement: Demonstrated the first photonic processor running state-of-the-art neural networks including ResNet, BERT, and deep reinforcement learning algorithms without modifications. Their six-chip package delivers 65.5 trillion operations per second while consuming only 78 watts of electrical power plus 1.6 watts of optical power.

Tsinghua's Taichi Chip: Achieved 160 TOPS/W energy efficiency with 91.89% accuracy on image recognition tasks, representing over 1,000 times more energy efficiency than electronic counterparts.

Startup Success

Several startups have achieved significant milestones:

  • Lightmatter: Achieved $4.4 billion valuation after raising $400 million in 2024
  • PsiQuantum: Raised $940 million from the Australian government in 2024
  • Q.ANT: Secured €62 million Series A and became the first company to ship commercial photonic processors as standard PCIe cards
  • Photonic Inc.: Received $100 million investment from Microsoft

AI Applications

Photonic computing delivers three key advantages for AI workloads:

  1. Massive Parallelism: Matrix multiplication operations can be performed passively in optical components at the speed of light
  2. Ultra-low Energy Consumption: Up to 1,000x more power efficiency than digital networks
  3. Speed: 2 to 3 orders of magnitude faster than traditional electronic processors

Real-world applications include computer vision for autonomous vehicles, natural language processing with Transformers, wireless communications for 6G systems, and scientific computing for climate modeling.

Conclusion

Photonic computing achieved commercial viability in 2025, offering compelling alternatives to traditional electronic processors for specific AI acceleration tasks. While it won't replace electronic systems entirely, it has become essential for heterogeneous computing architectures where energy efficiency, processing speed, and parallel operations provide real value.

The technology has moved beyond promising research to deliver practical solutions for real-world AI workloads, marking the beginning of a new era in computational efficiency and performance.

References

#AI