Technology
How neuromorphic computing works.
Spikes, not multiplies.
Traditional processors perform billions of multiply-accumulate operations every cycle. Neuromorphic processors communicate through discrete spikes — binary events that propagate only when needed.
No spike, no computation, no energy. A neuromorphic chip doing nothing consumes almost zero power.
Learning happens on-chip.
When two neurons fire in close succession, the connection between them strengthens or weakens — the same mechanism your brain uses to form memories. No cloud. No retraining. The chip adapts in real time.
1000x more efficient.
By only computing when spikes occur, neuromorphic processors achieve orders-of-magnitude better energy efficiency for inference. No idle power. No wasted cycles. The architecture mirrors the brain's own energy model.
Why it matters.
AI's power consumption is growing faster than our ability to generate clean energy. Neuromorphic computing offers a path to intelligent systems that don't cost the planet. Real-time learning at the edge. Sensors that think for themselves. Intelligence without the electricity bill.
Python to silicon.
Our SDK lets you define spiking neural networks in Python, simulate them on CPU or GPU, and deploy to real hardware. One codebase, three backends. What you test is what you get on silicon.
View SDK on GitHub →