Lomare builds silicon that draws probability directly from device physics — maintaining a live belief about the world at a small fraction of the energy digital hardware spends imitating the same thing. From always-on sensors to inference at datacenter scale.
Every battery-powered intelligent device faces the same constraint: it cannot afford to run a neural network continuously. So the industry works around it — duty cycling, threshold triggers, heuristic wake-up logic. These reduce average power, but they buy that saving by giving up awareness.
Between scheduled wake-ups the device is not merely idle — it is unaware. Events that fall in the gap are simply never seen, and no amount of downstream model accuracy recovers them.
Fixed triggers respond to instantaneous amplitude, not meaning. They drift out of calibration with the environment, and must be tuned conservatively — which means waking on noise.
Each unnecessary escalation costs milliwatt-scale inference. In deployments where events are genuinely rare, most of the energy budget is consumed confirming that nothing happened.
Probabilistic workloads are expensive on conventional hardware for a specific reason: randomness has to be manufactured. A pseudo-random number generator, floating-point arithmetic and repeated memory accesses are spent producing each effective sample — picojoules to nanojoules of energy to imitate something physics does for free.
Memory devices are already stochastic. Their switching behaviour is probabilistic by nature, and conventional design treats this as a defect to be suppressed. Lomare's patented primitive does the opposite: it calibrates that physical randomness into a controllable sampling operation, so probability becomes a native quantity in silicon rather than an arithmetic result.
Rather than waking on a threshold, the system holds a continuously updated probabilistic belief about a latent variable, and escalates only when the evidence justifies it. Heavy inference still happens — just far less often, and for far better reasons.
A probabilistic filter ingests raw sensor input and maintains a posterior over the state of the world, updated incrementally with every observation. Never asleep, never blind.
Escalation is driven by belief drift and uncertainty, not signal amplitude. Environmental noise is rejected at the belief layer, before any classifier is invoked.
When the posterior crosses a decision boundary, a downstream neural network wakes, resolves the event, and returns to deep sleep. The MDG is agnostic to the model it gates.
The relevant unit for always-on inference is not operations per second — it is the energy cost of one belief update. That is where physical sampling separates from arithmetic emulation.
Belief-gated silicon fits deployments where events are rare, energy is finite, and missing the moment is expensive.
Multi-year deployments on a single cell, with no blind intervals between wake-ups.
Escalation on accumulated evidence rather than vibration amplitude, on energy-harvested power.
Continuous acoustic awareness at a fraction of always-on feature extraction, viable in hearables.
Persistent attention instead of blind intervals, over multi-year solar or disposable deployments.
Physiological awareness without the wake-on-every-frame penalty of current biometric front-ends.
Environmental noise rejected in the belief layer, before the classifier is ever invoked.
The workloads defining modern AI — generative models, Bayesian methods, reinforcement learning, reasoning under uncertainty — are sampling-heavy at their core. Deterministic accelerators manufacture that randomness with arithmetic, and a growing share of AI's energy bill is spent doing so.
Hardware that samples natively changes the economics. Belief-gated sensing at the edge is the first product of a primitive whose larger destination is datacenter-scale probabilistic inference — sampling as a first-class operation in the fabric of AI compute, not an emulated one.
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