We put the mechanisms
of the human brain
into today's silicon.
Selective memory for datacenters. Associative recall for knowledge. Instant reflexes for machines.
Three engines · one brain · running on silicon that ships today
A brain does three things well. So do we.
It keeps close only what it needs. It retrieves the rest by cue, all at once. And it reacts to change before it has finished looking. Each of those is an engine — and each runs on hardware that already exists.
A KV-residency policy engine. The hot cache stays in HBM; the rest demotes to SSD during idle windows. The decode path is never touched.
A parallel cued-retrieval engine. Wikipedia-scale knowledge, hallucination-free, on standard GPUs — no new hardware.
An event-vision reflex engine. It sees only change, decides at milliwatts, and moves the robot before the object arrives.
Zero storage I/O during decode.
Inference engines run the model. NVIDIA's transport layer — NIXL / KVBM, RDMA with CPU bypass — lays the road. XHBM is the policy layer on top: it decides what stays resident, when to move it, and how to demote it.
The discipline is what makes it safe. XHBM never touches the decode path. It acts only inside idle windows, where the GPU is already waiting. It attaches to the NIXL standard with no code changes on the adopter's side, and it is fully reversible — switch it off and the stack is exactly what it was.
HBM is the most expensive and most scarce resource in the AI datacenter. XHBM lets the same HBM serve roughly twice the concurrent users without adding a single GPU. At industry scale, that is a software layer standing in for billions of dollars of memory and GPU buildout — capacity bought back from hardware that is already installed.
Zero hallucination, 10.45 milliseconds.
Hear “red round fruit” — and “apple” arrives before the sentence finishes. The brain has not searched a database or walked a graph. One cue radiates across everything stored, and only the memories that match fire back.
Recall is that mechanism as an algorithm. The mathematics turns out to be elegant: retrieved = softmax(β · Q · KT) · V — the same operation inside every transformer attention head, mapped onto the right memory hierarchy. HBM becomes the hippocampus, holding the cue. NVMe becomes the cortex, holding the patterns. A query no longer walks the memory bus N times; it fires across the whole content store at once.
Output is preserved bit-exact — twice the concurrent users on the same hardware, and not a single bit of the answer changes.
Today's LLM session holds roughly nineteen books' worth of context. Recall handles six thousand books in the same session with zero hallucination — the entire English Wikipedia, 6.4 million articles, on a single GPU node, with no measured accuracy loss. An enterprise's whole knowledge base can sit inside one conversation instead of being paged through it.
NVMe · cortex
BGE-M3 embeddings
It doesn't watch. It reflexes.
An event camera sees only change. A static background produces no data at all, and latency is measured in microseconds rather than frames. A neuromorphic chip then decides at milliwatts.
On top of those two, React detects a fast-approaching object early, predicts where it is going with Kalman tracking and a looming time-to-contact estimate, and moves the machine before impact. Same perception, swap the policy: the robot that dodges becomes the robot that catches.
What happens when you throw a ball at a robot?
Most robots simply get hit — their eyes are too slow. We took a commercially sold robot dog (Unitree Go2), changed not a single line of its walking AI, and attached only the reflex. All we send is one velocity command — “step aside” — in the exact format the Unitree SDK already accepts, so it drops straight onto the real robot.
The outside movie camera. The line trailing the ball traces its path.
The bird's-eye view. The clearest way to read how far the robot actually side-stepped.
The world as the robot's eye sees it. Only motion is visible; the static background is zero data. Less to see means faster thinking and almost no power. The box is a detection, the arrow is the predicted position 0.1s ahead.
The robot's state of mind: IDLE (all clear) → TRACK (something incoming) → EVADE (move). TTC is the robot's own estimate of time to collision, computed before a person could say "huh?".
Physics simulation (MuJoCo). Robot model, mass and locomotion policy are all real Go2 spec. TTC is shown as estimate and ground truth side by side, because we would rather measure honestly. Next step: the physical robot.
Because the reflex runs under a watt — brain power, not a tens-of-watts GPU — a battery-powered drone or a walking robot can keep evasion and safety switched on all the time instead of only when the budget allows. Reflexes as standard equipment on every edge machine: that is the market this engine opens.
Anything that moves — and anything that moves toward it.
The demo is a robot dog and a ball, because that is the cleanest way to show a reflex. But nothing in the engine is about dogs, or about balls. What it does is far more general: detect something on a collision course, estimate when it arrives, and act before it does. Every machine that shares space with moving things needs that, and today almost none of them can afford it.
It is a module, not a product line — an event sensor, a sub-watt chip, and a policy. Adding it does not mean redesigning the machine, retraining its control stack, or waiting for the next hardware generation. That is why the list of places it belongs keeps growing rather than narrowing.
Birds, branches, cables, another drone in the same corridor. Inspection under bridges and inside tunnels where GPS is gone. Disaster response and search is one obvious case — but it is an example, not the boundary.
At line speed the decisive moment falls between two frames of an ordinary camera. A reflex that fires inside that gap can reject a foreign object, catch a jam as it starts, or divert a part without slowing the belt.
Same perception, opposite policy. Evade the person who steps into the cell; intercept the part that is handed over. A safety layer that runs continuously instead of a light curtain that stops the line.
Mobile robots in aisles shared with people and forklifts. Falling boxes, dropped pallets, a cart pushed around a blind corner — the events that a fixed schedule can never anticipate.
Swinging loads, falling debris, machinery in motion, dust and vibration that blind a frame camera. Change-only vision is exactly the sensor for scenes where most of the picture is irrelevant.
The reason this list is long is the power budget. Under a watt, a reflex is always on. At tens of watts, it is a feature someone eventually switches off — and then it may as well not exist.
We are deliberately not choosing one vertical to name as the market. A reflex is infrastructure — the way a brake or a shock absorber is infrastructure — and the machines that need one are not a category, they are a majority.
A datacenter that remembers selectively. A knowledge system that recalls associatively. A machine that reacts instantly. Three engines, one brain — and in every case the silicon is someone else's, already shipping, suddenly doing more.
A fly, with a speck of a brain, evades the fastest human hand.
It is not winning on compute. A fruit fly has about a hundred thousand neurons and a power budget measured in microwatts. It wins on structure — it keeps only what it needs, recalls by cue, and reacts to change rather than to pictures.
Von Neumann separated memory from compute, and every chip built since — CPU, GPU, TPU — inherited that boundary. Frontier scale is now colliding with the wall it implies: the cost of moving data exceeds the cost of computing on it, and memory costs more than the silicon that uses it. The next watt of efficiency is no longer hiding in the transistor. It is hiding in the structure.
So we don't build hardware. We implant that structure, and the hardware you already own does more. GPU, SSD, neuromorphic silicon, event sensors — the best silicon at every layer, with UmpaRumpa's brain layer on top of it.
Let your silicon do more.
Measured results, the integration path on NIXL, and what a two-week PoC looks like on your stack.
The full clip, the latency budget, and what it takes to put the module on your platform.
Sunnyvale, CA 94085