We make
memory think.
The brain doesn't win by storing more. It wins by deciding — what to remember, what to forget, what to recall. We build that decision layer for today's memory hierarchy, in software. No new hardware required.
Between compute and storage · running on silicon that ships today
Memory got faster. It never got smarter.
Thirty years of memory engineering built a magnificent hierarchy — HBM, DRAM, NVMe — each layer faster, denser, cheaper per bit. But the decision of what goes where is still made by rules written in the 1980s: least-recently-used, first-in-first-out. Blind rules, managing the most expensive real estate in computing.
AI pays the bill. GPUs sit idle waiting on memory. HBM — the costliest silicon ever shipped — spends its capacity holding data nobody will touch again. The compute layer is crowded. The storage layer is crowded. The layer that decides is empty.
A 20-watt brain manages memory three ways. We moved all three into silicon.
The brain's genius is not capacity. It is management — three mechanisms, refined over half a billion years. These are not metaphors. They are algorithms, and they run on the memory hierarchy you already own.
A day's memories accumulate in the hippocampus; sleep moves them to the cortex. The brain never reorganizes while it is thinking.
Memory is not searched. One scent of tangerine and a grandmother's house arrives whole — related memories surface together, in parallel.
A ball flies at your face and your hand is already up. The signal never reaches the brain — the spinal cord answers first.
XHBM — HBM as the hippocampus. SSD as the cortex.
The brain moves the day's memories from hippocampus to cortex while you sleep — never while you think. XHBM applies the same discipline to the most expensive silicon on earth. It watches what an LLM is actually using, keeps the hot working set in HBM, and demotes the cold remainder to high-density QLC SSDs only inside idle windows, where the GPU is already waiting.
During decode it moves nothing — not one byte — for the same reason the brain doesn't reorganize mid-thought. It attaches to NVIDIA's NIXL / Dynamo rails 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 — and every LLM operator's serving bill, cut by the layer they didn't know was missing.
Recall — memory that surfaces itself.
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: 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. No index walk, no vector-database pilgrimage: one associative readout across everything the machine has ever stored. And a memory that doesn't exist cannot surface — so it doesn't hallucinate.
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. This turns every terabyte of commodity NAND into usable AI memory: long-term memory stops being a database problem and becomes what it is in the brain — just another tier.
NVMe · cortex
BGE-M3 embeddings
If the principle is real, it should survive on one watt. It does.
When a ball flies at your face, the signal never travels to the brain — the spinal cord answers first, because the round trip would arrive too late. React is that reflex arc in silicon. Nothing is sent to a cloud or a GPU: an event camera — an eye that sees only change — and a 91KB reflex model respond inside the machine itself.
No GPS, no radio, under a watt — and it evades objects at speeds it never trained on, 94%+ of the time. The same decision layer that manages a datacenter's memory, compressed six orders of magnitude in power. Now being validated on real drones.
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 quadruped robot, 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 robot’s own SDK already accepts, so it drops straight onto the real robot.
Physics simulation (MuJoCo). Robot model, mass and locomotion policy all match the real robot’s spec. TTC is shown as estimate and ground truth side by side, because we would rather measure honestly.
A swarm that never collides — on reflexes alone.
No GPS. No radio link between drones. Each drone avoids every other using nothing but its own event-camera reflex — the same engine, running per machine. When positioning and comms drop out, the reflex is what is still working.
Because the reflex runs under a watt — brain power, not a tens-of-watts GPU — a battery-powered machine can keep evasion switched on all the time instead of only when the budget allows. And because it is the same selective-processing principle that runs XHBM, that range — six orders of magnitude in power — is itself the proof of the technology.
The brain never processes everything. Neither should silicon.
Compute belongs to the GPU makers. Storage belongs to the memory makers. We are the layer between — the one that decides. It consolidates when idle, recalls by association, and reacts before thinking.
The next watt of efficiency is no longer hiding in the transistor. It is hiding in the structure. In biology and in silicon alike, attention is cheaper than capacity.
Put a brain behind your memory.
Measured results, the integration path on NIXL, and what a PoC looks like on your workload, on your hardware, in weeks.
The full clips, the latency budget, and what it takes to put the module on your platform.
Sunnyvale, CA 94085