CRYSTALLUM AI

Crystal-Based Photonic Hardware for Edge AI Inference

Silicon Has a Ceiling

The global AI industry is approaching fundamental physical limits in semiconductor performance, energy, and thermal management.

10× AI energy demand projected to multiply every 2 years, outpacing efficiency gains
2nm Silicon transistor nodes approaching quantum tunneling thresholds—no viable path beyond
40% Of edge AI deployment cost attributed to thermal cooling infrastructure alone

Photonic Crystal Architecture

Two proprietary compute engines built on non-von Neumann photonic principles.

Photonic Crystal Inference Accelerator
  • Optical matrix multiplication at speed-of-light propagation, eliminating DRAM bottlenecks
  • 3D photonic bandgap lattice operating at 1550nm wavelength for zero crosstalk inference
  • Sub-femtojoule per operation energy profile — 200× below NVIDIA H100 baseline
Liquid Crystal Matrix Engine
  • Dynamically reconfigurable weight storage via applied electric field, no erase-write cycle
  • Analog in-memory computation across 4096 parallel inference channels per tile
  • Operates at ambient temperature — eliminates cryogenic or active cooling dependency

The Edge AI Market

$60B
Projected market size by 2030 · Target vertical: Industrial Autonomous Drones
Metric Current Silicon Crystallum AI
Power Draw 250–400W per unit 1.2W per unit
Cooling Required Active liquid / forced air Passive ambient
Inference Speed 12–80ms latency <0.4ms latency

Build the Future With Us

We are actively seeking research collaborators, deep tech investors, and strategic industry partners to accelerate commercialization of photonic AI hardware.

Research Institutions
Joint IP development, fabrication access, and co-published validation studies in photonics and AI.
Deep Tech Investors
Series A open. Seeking aligned capital with deep tech hardware thesis and 7–10 year horizon.
Strategic Partners
OEM integrations, defense primes, and industrial drone manufacturers for early deployment pilots.
research@crystallum.ai