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.

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