optical

**Optical Neural Network Photonics** is **implementing neural networks using photonic components (waveguides, phase modulators, photodetectors) achieving low-latency, energy-efficient inference** — optical computing for AI. **Photonic Implementation** encode data in photons (intensity, phase, polarization). Waveguides route optical signals. Phase modulators (electro-optic) perform weighted sums. Photodetectors read outputs. **Analog Computation** photonic modulation inherently analog: phase shifts implement weights. Matrix multiplication via optical routing and interference. **Speed** photonic modulation at GHz speeds (electronics much slower). High throughput. **Energy Efficiency** photonic operations consume less energy per multiplication than electrical. **Integrated Photonics** silicon photonics integrate components on chip. Waveguides, modulators, detectors. Compatible with CMOS. **Wavelength Division Multiplexing (WDM)** multiple colors on single waveguide. Parallel channels. **Mode Multiplexing** multiple spatial modes increase parallelism. **Scalability** thousands of neurons theoretically possible on single photonic chip. **Noise** shot noise from photodetection limits precision. Typically ~4-8 bits. **Programmability** electro-optic modulators electronically tuned. Weights updated electrically. **Latency** photonic propagation ~150 mm/ns. Lower latency than electronic networks. **Activation Functions** nonlinearity via optical nonlinearity (Kerr effect, free carriers) or post-detection electronics. **Backpropagation** training via iterative updating. Gradient computation challenging optically. **Commercial Development** Optalysys, Lightmatter, others developing. **Benchmarks** demonstrations on MNIST, other tasks. Inference demonstrated; training less mature. **Applications** data center inference, autonomous driving, scientific simulation. **Optical neural networks offer speed/energy advantages** for specialized workloads.