Edge AI for autonomous robotic systems and defenseChandler, Arizona
Cloud-class AI, pocket-sized.
Morphos builds edge AI frameworks that bring high-capability AI into form factors small enough to go where the cloud can’t. On the machine. Off the grid. Ready when the mission changes.
The problem
The machines are coming. Their brains are still in the data center.
AI is moving out of the data center and into machines that have to think for themselves. The problem is that the most capable AI still depends on data-center-scale memory, power, and cooling. Field systems get smaller models trained in advance, and teaching them something new usually means collecting data, retraining, and redeploying software across the fleet. That can take weeks. The mission changes in hours.
The hard part isn't getting AI onto the machine. It's changing what the machine knows once it's out there.
Whatever the battery gives you.
Every gram costs range or payload.
No cooling beyond the air around it.
Often slow. Sometimes gone.
Retraining takes weeks. A mission takes hours.
Everything the machine knows has to fit on board.
The solution
Change the mission. Keep the machine.
DarkStar is built for machines that have to adapt after deployment. Instead of rebuilding the entire AI stack when the mission changes, operators can deploy validated mission-specific intelligence directly to the edge.
Update the mission. Keep the platform.
When the model has to change
Collect new examples, retrain the model, test the new version, then push it across the fleet. That can take days or weeks—and usually requires significant compute and engineering work.
WITH DARKSTAR, WHEN THE MISSION CHANGES
Prepare the mission update, validate it, and deploy it to the machine—without a fleet-wide retraining and software-redeployment cycle.
Intelligence that moves at mission speed.
DarkStar is designed to keep the onboard intelligence footprint compact, reducing the memory and compute burden carried by the platform.
Core AI processing and decision support happen locally. The machine can operate without a cloud connection.
Deployed systems can adapt to new mission requirements without a full model-retraining and fleet-redeployment cycle.
Green Vectors gives DarkStar the efficiency profile needed for constrained hardware, dramatically reducing the workload footprint compared with conventional approaches.
Building your own hardware? DarkStar can also integrate directly into platforms you already build through our SDK. EdgeRunner is the fastest way to deploy the complete system, but it isn’t the only way.
The hardware
Meet EdgeRunner.
EdgeRunner puts DarkStar into a compact, self-contained compute unit you can mount directly on a machine. It connects to the platform’s existing sensors and systems and runs the DarkStar intelligence stack locally—without depending on a cloud connection. Put it on a drone, robot, vessel, vehicle, or fixed sensor. The intelligence stack stays the same. The platform is up to you.
Swap a Cartridge.
Change the Mission.
When the machine needs to recognize or handle something new, load a different EdgeRunner Cartridge containing the knowledge for that mission. No model retraining. No new software build pushed across the fleet.
EdgeRunner Shell
The physical compute unit. Processor, memory, storage, interfaces, and the DarkStar runtime in one compact enclosure designed to connect with the platform’s existing sensors and systems.
EdgeRunner Cartridges
The machine’s mission memory. Each Cartridge is a mission-specific intelligence package configured for a defined operational task. Change the Cartridge, and the same EdgeRunner platform is ready for a different mission.
EdgeRunner OS
The control layer connecting EdgeRunner to the platform’s sensors, autonomy stack, and operator interfaces. It coordinates the DarkStar runtime with the host system.
Where it goes
Built for the places the cloud can't be counted on.
Any platform that has to see, understand, and decide on its own—with limited power, limited space, and connectivity it can’t rely on.
Hover or tap a platform to see what changes for it
Target applications. Integration depends on the platform, the sensor, and the task.
What that makes possible
Put it all together and the machine stops being a sensor waiting for instructions. It becomes something you can give a job to.
Not a flying camera phoning home. A quadcopter that recognizes, decides, and remembers on its own, on a battery, in a place with no signal.
Every manual, procedure, map, and lesson learned for a site, on the machine and searchable in the moment. We’ve already demonstrated the underlying idea by putting AI searchable Wikipedia on a Raspberry Pi.
Load new mission memory and every machine can recognize something new without rebuilding the entire AI stack.
Field observations can feed a controlled review-and-update cycle, so validated lessons from one deployment can improve future fleet releases.
When the thinking happens on board, nothing has to be streamed anywhere. A platform can operate under emission control, transmitting only conclusions, rarely, or nothing at all. Less to intercept. Less to jam. A smaller signature.
The breakthrough behind DarkStar
The breakthrough is what fits on the machine.
A vector is how AI turns information into numbers so it can compare meaning and similarity. Modern AI systems can store millions or billions of these representations as searchable memory.
AI systems can draw on enormous amounts of searchable knowledge. The challenge is making enough of that capability practical on constrained hardware.
That is what Green Vectors does. Instead of treating every new piece of information as another independent vector to store and search, Green Vectors continuously organizes knowledge into compact semantic representations.
- It organizes. Related information is grouped around shared meaning, so redundant knowledge doesn't have to become another standalone vector every time it appears.
- It weighs. Not every piece of information should influence memory equally. Green Vectors can weight new information by relevance, frequency, recency, or other signals as it becomes part of the memory.
- It keeps everything connected. Large sources are organized hierarchically, keeping specific details connected to the larger documents, sections, and ideas they came from.
The result isn't conventional compression. It's a different way of organizing vector memory. In our Project Gutenberg benchmark, an index built from roughly 15 million traditional vectors fell from 260 GB to 1.3 GB—a 99.5% reduction—while retrieval results moved closer to the correct answer rather than farther away.
Green Vectors provides the efficiency layer. DarkStar brings it to edge intelligence. EdgeRunner turns it into deployable hardware.
Why we expect it to travel: Green Vectors operates on vector representations, not on one particular kind of source data. Text, imagery, sensor data, and other inputs can all become vectors. That makes the architecture portable across workloads; performance on each platform still has to be measured and validated.
How far can the same memory travel?
Memory, not disk. To answer fast, a memory has to be resident on the platform, and that's the scarcest thing on board. Here's what fits where, using the measured sizes, from a data center down to a handheld.
At 50,000 books, the conventional memory needs 264 GB and fits only a data center. After 1-bit quantization it needs 12.1 GB and fits down to an autonomous platform, with accuracy given up to get there. Green Vectors needs 5.3 GB and fits all five, down to a handheld.
Footprints are the measured sizes from the 50,000-book study (260 GB conventional, 8.1 GB after 1-bit quantization, 1.3 GB Green Vectors) plus a 4 GB allowance for the model and runtime. Larger collections scale the measured sizes proportionally, which is an assumption. Memory budgets are selected examples, not device specifications. Source-document storage is not included. This shows what fits in memory, not demonstrated deployment, power, or thermal performance. The quantization tradeoff shown here describes this comparison, not every quantization method. More on the Green Vectors page
Measured on public and enterprise data. The first patent application has been allowed. In use with evaluation partners through our SDK.
The architecture is patent-pending. Benchmarks on embedded hardware are underway, with first results expected this year.
In prototype, with evaluation programs open to defense and autonomy partners.
Work with Morphos
Tell us where your machines need to think.
Air, ground, surface, subsea, or a sensor on a hillside. Bring the platform, the task, and the limits it has to live within. We'll bring the intelligence.