LoMaRe's circuit turns randomness into a tunable distribution — the sampler that probabilistic compute has been imitating in software.
Keys, nonces and signatures are only ever as strong as the entropy behind them. brAIn is a drop-in hardware entropy source — the block that sits at the root of every secure chip. Developed in a standard chip process at room temperature, it integrates into secure elements, MCUs and HSMs — at orders of magnitude lower energy per bit than existing entropy circuits. Quantum resistance by design — ready for the post-quantum cyber security era.
Confidence costs compute, so most edge devices skip it — and act on guesses they cannot grade. Modern policies generate each action by sampling, step by step; that sampling is exactly what brAIn does natively — accelerated, not emulated. Knowing when it does not know becomes affordable: many more possibilities per watt, or the same accuracy on a fraction of the power.
A robot in a field or a human space cannot be pre-programmed. Working out where it is, it tracks thousands of possible positions at once from noisy sensors, every cycle. Deciding what to do next, it tests thousands of candidate moves per second to pick one that will not hit anything. With brAIn every one of those possibilities is a device, not an arithmetic workload — more options explored in the same budget, better choices in clutter.
Edge to datacenter: a compute-in-memory architecture with randomness and compute in a single array runs sampling-bound AI natively — Monte-Carlo, Bayesian and generative sampling, not emulated — at a fraction of a GPU's energy. It targets the memory-bound decode stage of inference, where the industry runs memory-light accelerators, and is delivered as licensable IP or silicon (PCIe accelerator cards).
The same sampler serves all three: certified entropy at the root of every secure chip, uncertainty-aware edge AI (including robotics, wearable, medical devices and others) that weigh thousands of possibilities per cycle, and compute-in-memory inference acceleration from the edge to the datacenter — one IP, delivered as licensable IP or silicon.
As power becomes the constraint, energy per bit becomes the product. We start from the most efficient primitive: >100× lower energy, measured in silicon — ~10 femtojoules per true-random sample, orders of magnitude below software, measured on real devices. The whole industry is racing to cut energy per bit. We begin there by construction — true randomness in silicon, at a fraction of a GPU's energy.
One IP platform, three applications — the same primitive that secures a key can weigh a robot's next move and run sampling-bound inference without the energy bill.
A drop-in hardware entropy source — the block at the root of every secure chip. Standard process, room temperature; integrates into secure elements, MCUs and HSMs at a fraction of the energy per bit. Comes as licensable IP sitting next to the existing SOC architectures.
Talk to us about TRNG →Perception, planning and policy sampling for robots, autonomy stacks and humanoids — machines that must act under uncertainty, on a power budget. Many more possibilities per watt; knowing when it does not know becomes affordable. Evaluation kits ship 2028.
Pilot programmes →A compute-in-memory accelerator that runs sampling-bound AI natively — Monte-Carlo, Bayesian and generative sampling, not emulated — for quant desks, national labs, pharma and cloud AI. Licensable IP or silicon.
Licensing →Probabilistic workloads are expensive on conventional hardware because randomness has to be manufactured — a PRNG, floating-point arithmetic and memory traffic for every sample. A memristor pulsed on the microsecond scale answers unrepeatably; brAIn calibrates that physical randomness into a controllable sampling operation, so the posterior can be accumulated by physics instead of computed by logic.
Because the belief lives in analogue, always-on sensing costs what a pulse costs — microwatts, continuous — and the expensive digital stages stay asleep until a decision is actually due. The digital host is untouched: it receives a belief and an interrupt, and runs whatever model it already runs.
Talk to us about brAIn — for secure chips, edge AI, robotics or inference acceleration. Evaluation, licensing, or a joint development programme.
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