Photonic computing for robot AI — beyond electronics
Light-speed inference · Lower power · Higher throughput
Photonic Processors is POLCERO's R&D direction for accelerating AI inference in our robots using light instead of electronics alone. Optical matrix multiplication — the core operation in neural networks — can be performed in a single optical pass through photonic chips, delivering sub-nanosecond latency and orders-of-magnitude lower energy per operation compared to GPU/ASIC chips.
Honest positioning: Honest positioning: This is an R&D direction and vision — not a finished product. POLCERO tracks the photonic AI computing space as a future compute layer for our robots' AI brains. We engage in R&D projects and partnerships in this area.
TECHNOLOGY
How photonic computing works
Processing information with light rather than electrons alone.
Optical matrix multiplication
Neural network inference is dominated by matrix-vector multiplication (GEMM). Photonic chips perform this operation using light passing through Mach-Zehnder interferometer meshes, microring resonators or beam-splitter arrays — executing in a single optical pass what electronic chips require thousands of clock cycles to compute.
Hybrid photonic-electronic architecture
Photonic chips excel at linear operations (matrix math) but cannot natively perform nonlinear operations (activation functions). The consensus architecture combines optical cores for matrix math with electronic units for nonlinear activations and accumulation — a hybrid design that captures the best of both technologies.
BENCHMARKS
Photonic vs. electronic AI chips
Key properties — data from published research, validated benchmarks, as of 2025–2026.
| Property | Photonic | Electronic (GPU/ASIC) |
|---|---|---|
| Matrix multiply latency | Sub-nanosecond (single optical pass) | Microsecond–millisecond |
| Energy per MAC | Sub-picojoule (target) | 10–100 picojoules |
| Bandwidth | Terahertz-range | ~GHz-range |
| Heat generation | Very low | High (major bottleneck) |
| Nonlinear ops | Requires electronic hybrid | Native in silicon |
STATE OF THE ART · 2025–2026
Key players and milestones
Matter-of-fact overview — data as of research date, subject to change.
Lightmatter (USA)
Passage M1000: 114 Tbps photonic interposer; $400M Series D 2025; first photonic to run transformers, CNNs and RL without modification.
Q.ANT (Germany)
~50× performance advantage vs. NVIDIA GPU in matrix-vector multiplication benchmarks, validated at LRZ supercomputing center.
Neurophos (USA)
1,000×1,000 optical matrix chip demonstrated; claims 10× NVIDIA Vera Rubin NVL72 in FP4/INT4 workloads at similar power.
Celestial AI → Marvell
1.6T light engines for AI networks — acquired by Marvell for $3.25B (Dec 2025).
MARKET SIZE
$1.8B → $14.6B
Global photonic AI accelerator chip market, 2025 → 2034 (CAGR 26.3%). 52 photonic computing companies tracked globally as of 2026.
POLCERO DIRECTION
Photonics as the future compute layer for robot AI
As photonic inference accelerators reach commercial maturity (2027–2031 timeframe for datacenter-class products), POLCERO's AI-brain architecture is designed to absorb them — the model abstraction layer allows swapping the compute substrate without retraining the task intelligence. We engage in R&D projects and partnerships to evaluate photonic chips as the compute layer for edge inference in our robots — where power and latency are the binding constraints.
Interested in photonic R&D collaboration?
We engage in R&D projects and partnerships.
If you work in photonic AI, silicon photonics or adjacent compute technology, reach out to discuss research collaboration or pilot projects.