Binary was a historical accident. Neural networks think in three states. We built silicon that thinks the same way.
Nikolay Brusentsov's team built the Setun — the world's first ternary computer — in a single room at Moscow State University. 18-trit words. Half the components of a binary machine doing the same work. 2.5× cheaper.
50 units produced between 1959 and 1965. It ran cooler. It failed less often. It used less electricity. And then: nothing. The Soviet administrative apparatus chose binary semiconductors — not because ternary failed, but because the momentum of the transistor era was already committed elsewhere.
Brusentsov died December 4, 2014. His machine was right. The industry was wrong. We are finishing his work.
Neural network activations cluster around −1, 0, +1 naturally. ReLU outputs zero or positive. Sign functions output −1 or +1. Weight distributions center on zero. This isn't a coincidence — it's the signal.
Binary forces these signals into two states. The result: clipping, precision loss, power wasted on redundant state management. A trit encodes 1.585 bits of information — 60% more per digit. For the same numerical range, you need fewer trits than bits. Fewer digits means fewer operations means less energy per inference.
Binary isn't optimized for how brains compute. Ternary is. Every millisecond of on-device inference on a pendant that fits on your collarbone, you pay for binary's legacy inefficiency. We don't.
Every layer of the stack is built for trits — not retrofitted from binary. Silicon → Firmware → OS → Applications. Omi runs on the full stack. No translation layer. No abstraction penalty.
The 5500FP is our first silicon tapeout. Custom RTL. LLVM toolchain. Balanced ternary ISA with native trit arithmetic. Python simulator so you can develop without hardware. Open firmware. You own your device, fully.
Omi is the first product running on native ternary silicon. Not a binary chip running ternary emulated software — a real ternary processor doing real ternary math for a real wearable device on someone's body.
LUMINA is our formal architecture framework — not a marketing term, but a cryptographic design discipline. Every layer is accountable to the layer below it. Privacy is not a feature. It is a property of the system.
LUMINA stands for DTIA: the Defense-grade Trusted Integration Architecture. It means the hardware cannot be made to betray the user — even by whoever built it. No manufacturer backdoor. No OTA update that silently reverts the privacy boundary. The silicon enforces the contract.
Yunisa Stack is the hardware substrate — the 5500FP and its firmware. YunisOS is the OS layer that runs on the hardware, exposing a controlled API surface. YunisAI is the AI/ML runtime — inference engine, model loader, and privacy enforcement layer combined. You never touch YunisOS or YunisAI directly. Omi is the consumer interface.
The conventional AI stack is: silicon from ARM → OS from Google → model from OpenAI → service from AWS → your data flowing through all of it, owned by none of them. Every hop is a privacy risk. Every vendor is a compliance surface. Every update is a policy change you didn't agree to.
Sovereign AI means you own the entire stack. From the transistor level up. When inference runs on Omi, it runs on silicon we designed, firmware we wrote, an OS we control, and a model we trained. No ARM license. No Android. No cloud. The data never leaves the hardware — not because of a promise, but because there is no path out.
On-device inference is not a fallback mode. It is the only mode. Omi does not have a cloud API. The 5500FP has no wireless connectivity by design. Privacy is a hardware property, not a policy.
Software promises to respect privacy. Hardware enforces it. On Omi, the microphone is hard-wired to a visible LED. When the LED is off, the mic is physically disconnected. No software update can change this.