Autonomous Systems
Keep critical information close to the system that needs it.
Vehicles, autonomous platforms, sensors, and field systems using AI cannot always depend on a fast, stable path to the cloud. Morphos is researching compact information systems for perception, sensor data, and local decision support on the hardware itself.
For platform, integration, and research discussions.
Mission constraints
Some decisions cannot wait for a round trip.
A remote system may have limited bandwidth, face network delays, or lose its connection. When a decision has to happen now, the information and processing needed for it must be closer to the platform.
That platform cannot add memory, power, cooling, or physical space on demand. The AI has to work with what is actually there.
The hardware is already in the field. The intelligence has to fit.
What this enables
Keep more of what matters close enough to use.
The purpose of compact local information is not simply to reduce storage. It is to help a deployed AI system maintain a useful, current picture of its environment and task within the resources actually available.
- 01
More useful information onboard
Use finite memory for distinct, task-relevant information instead of repeated observations.
Continuous input - 02
A local picture that stays current
As new sensor data arrives, incorporate meaningful changes into the system's current state instead of allowing every observation to accumulate as another permanent entry.
Meaningful change identified - 03
Continuity when the network cannot be trusted
Keep relevant information available to perception and decision-support systems when centralized compute is delayed, bandwidth-limited, or unreachable.
Compact local state updated - 04
More capability within the same hardware envelope
Create room to evaluate more capable functions without assuming the platform can add memory, power, cooling, or physical space.
Decision support
Observe continuously. Retain selectively. Keep the next decision close.
Status: these are development objectives, not published performance claims. Each must be established separately for the target platform, workload, and task.
Research areas
Where Morphos is applying this approach.
Three research areas for information that arrives continuously and changes in value over time.
- 01
Perception
Representing important changes in visual and multi-sensor AI input without treating every frame, region, or observation as equally useful.
- 02
Sensor and telemetry
Maintaining compact, current information from AI-focused data streams where long periods may contain little new information and rare changes may matter most.
- 03
Autonomous and edge platforms
Keeping current information and decision support close to the AI platform that needs it. Whether it be a drone, satellite, or cell phone.
Performance is established separately for each target platform, workload, and task.
Deployment spectrum
The same constraint appears at very different scales.
A portable rack can carry more compute than a vehicle, and a vehicle more than a handheld device. Each still has a finite amount of memory, power, cooling, space, and bandwidth available for AI.
Morphos is developing for systems across this range:
Centralized compute remains part of the system. The focus here is what has to keep working when access to it is limited.
Hardware partners
A useful partnership starts with real hardware.
Define the target platform, the workload it must handle, and the available resources before building the evaluation.
You bring
- Target hardware and access
- A representative workload
- The known system limits
- An integration owner
We bring
- A software or model build appropriate to the evaluation
- Integration support
- A repeatable test setup
- Technical analysis of the result
We define together
- The current baseline
- Success criteria
- Test conditions
- The next decision
The outcome is a documented comparison and, if the criteria are met, a deployment plan.
Test plan
Test on the target platform.
Each evaluation measures four things.
Memory required by the information and model during operation.
Retrieval or output time under the platform's real power and cooling limits.
Task-level quality compared with the current baseline.
Behavior as new information arrives and the system state changes.
Under evaluation. Morphos has not published non-language performance results.
Review
Technical review and confidentiality.
Specific architectures, configurations, and results are shared under appropriate confidentiality agreements and export review. Security and data-handling requirements are defined with the partner before an evaluation begins.
Get in touchStart with the system that has to keep working.
Tell us where it operates, what it must do, and the memory, power, cooling, or connectivity limit in the way. We will determine whether a focused evaluation makes sense.
Get in touch