Optical and Photonic Computing
What is Optical Computing? Using light instead of electrons to perform computations, potentially offering massive parallelism and energy efficiency.
Why Photonics for AI?
| Advantage | Description |
|---|---|
| Speed | Light speed computation |
| Parallelism | Many wavelengths simultaneously |
| Energy | No resistive heating |
| Bandwidth | High data rates |
Optical AI Companies
| Company | Approach |
|---|---|
| Lightmatter | Photonic chip (Envise) |
| Lightelligence | Optical matrix multiply |
| Luminous Computing | Photonic AI accelerator |
| Optalysys | Optical FFT/CNN |
| Celestial AI | Photonic fabric |
How Optical Matrix Multiply Works
Light in --> [Mach-Zehnder Interferometers] --> Light out
|
Encodes matrix weights
Analog multiply: Amplitude modulation
Analog add: Interference
Lightmatter Envise
- Photonic tensor cores
- Works with standard deep learning frameworks
- PCIe interface to existing systems
- Demonstrated ResNet-50 inference
Challenges
| Challenge | Status |
|---|---|
| Precision | 8-bit typical, improving |
| Integration | Complex packaging |
| Programming | New toolchains needed |
| Cost | Currently expensive |
| Non-linear ops | Use electronic for activations |
Theoretical Advantages
| Metric | Electronic | Photonic |
|---|---|---|
| Speed (matmul) | ns | ps |
| Energy/op | pJ | fJ |
| Parallelism | 1000s channels | 100,000s wavelengths |
Current State
- Prototype systems available
- Mostly inference-focused
- Hybrid optical-electronic common
- Active academic research
Timeline Predictions
| Milestone | Estimated |
|---|---|
| Commercial inference chips | 2024-2025 |
| Widespread datacenter use | 2027-2030 |
| Training systems | 2028+ |
Best Practices
- Follow for future potential
- Consider for extreme energy constraints
- Hybrid approaches most practical today
- Watch for production announcements
photonicsoptical compute
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