Multiverse Computing partnered with a national defense champion to deliver a sovereign, on-premises object detection capability for aerial asset monitoring. The project compressed a state-of-the-art detection model into a footprint that runs efficiently on defense-grade edge GPU hardware deployed inside the client's own infrastructure, with no dependence on external clouds, and was validated end to end through our five-phase optimization pipeline.
The Challenge
The client required a sovereign, on-premises AI capability for detecting aerial assets in imagery, runnable on edge GPU hardware deployed inside its own infrastructure. The goal was to drastically reduce model size and computational load while preserving operational detection accuracy, and to validate the result on defense-grade hardware. Standard, uncompressed detection models were too heavy for the deployment footprint and too dependent on cloud or central compute to fit the sovereignty and security requirements of the mission.
Our Solution
Multiverse Computing applied its proprietary optimization technology to a state-of-the-art object detection model (YOLOv8m) through a five-phase pipeline of profiling, base training, compression, healing and validation. The compressed Multiverse Computing model is deployable on defense-grade edge GPU hardware via ONNX and TensorRT, with model size and computational load reduced well within the client's targets and detection accuracy preserved within mission-critical tolerance.
- Five-phase optimization pipeline: profiling, base training, compression, healing and validation.
- 45% parameter reduction and 20% lower computational load versus the base detection model.
- Detection accuracy preserved within mission-critical tolerance.
- Production-ready artifacts in ONNX and TensorRT formats, deployable on defense-grade edge GPU hardware.
- Reusable sovereign optimization pipeline applicable to additional vision models in 2 to 3 weeks.
Results
Sovereign, on-premises capability with no external cloud dependency
Compressed detection model running on defense-grade edge GPU hardware
45% parameter reduction versus the base detection model
20% lower computational load on the deployment platform
Detection accuracy preserved within mission-critical tolerance
Reusable optimization pipeline ready for additional models in 2 to 3 weeks
Strategic outcome: a sovereign aerial detection capability that runs end to end on defense-grade edge GPU hardware inside the client's own infrastructure, preserves operational accuracy, and gives the client a reusable pipeline to extend the same approach to other vision workloads.
