ModelCat AI

Founded 2015

Build, retarget, and benchmark machine learning models directly on real edge hardware targets

You can use ModelCat to build, retarget, and benchmark computer vision models directly against real hardware constraints. Instead of training models in abstraction and facing failures during deployment, you specify memory, latency, and power limits upfront. The system analyzes your dataset or existing model, applies target hardware parameters, and autonomously configures production-ready models. You can test performance on actual silicon rather than simulated environments, reducing development cycles from months to days for edge devices, robotics, sensors, and factory automation equipment.

What ModelCat AI does

Upload custom datasets or trained computer vision modelsSet target memory, power, and latency limitsSelect target hardware chips for automated validationRun automated hardware-in-the-loop performance benchmarksRetarget trained models to alternate silicon architecturesExport production-ready model files for edge devicesHardware-aware model generation based on physical constraintsDirect benchmarking on physical silicon hardware farmsInput parameters for power, latency, and memory limitsModel retargeting across different chip architecturesAutomated dataset validation and issue detectionDirect integration with NXP eIQ for embedded microprocessors