Easily Implement Efficient Sensor Fusion and State Estimation Algorithms
Reduce engineering time and improve performance and reliability of robotic and automotive systems, by taking advantage of Signaloid's UxHw® technology. Ease implementation and performance of implementations ranging from Kalman filters for IMU data to particle filters for LiDAR. Deploy Signaloid's UxHw on platforms ranging from microcontroller (MCU) systems to high-performance edge systems such as Nvidia Jetson and DRIVE platforms.
Wide application range
Whether building instrumentation for industrial robots, implementing control systems for unmanned aerial or undersea vehicles, or extending your legacy robotics platforms with new sensors and AI/ML models, we provide implementations of our UxHw technology to fit your needs. Deploy UxHw on platforms ranging from existing microcontroller-based platforms, to high-end edge hardware.
Faster development, lower costs
Get to your final implementations for sensor-driven robotics systems quicker and with fewer engineering and design costs by taking advantage of Signaloid's UxHw's support for techniques ranging from hardware-assisted Bayesian inference to hardware-accelerated Gaussian Process prediction.
Easy integration
Signaloid's hardware modules implementing UxHw technology help you get to solution designs quicker. Use hot-swappable hardware modules (microSD, full-sized SD, and PCIe M.2) that take advantage of the ubiquity of both hardware and OS support for mass storage devices in robotics platforms.
Fewer than
0h
0h
Over
0%
0%
*Factor reduction in non-recurring engineering (NRE) software implementation cost based on Constructive Cost Model (COCOMO) cost estimation methodology. The reduction is an underestimate as the calculation does not include additional modifications required to the Monte Carlo implementation, such as GPU parallelization, that would be required to run the application interactively in real time.
Solutions
Choose how to integrate Signaloid’s UxHw into your technology, with deployment methods including custom hardware modules, such as microSD-sized hot-swappable compute-in-memory modules, or cloud-based solutions using our compute infrastructure.
Example Performance Validation
Achieve over 600x speedups in your uncertainty quantification compared with existing C, C++, and FORTRAN models computing Monte Carlo simulations. Interested instead in adopting data-driven AI/ML models to replace your hand-crafted C/C++/FORTRAN code? Use the AI/ML runtime systems which run on the Signaloid cloud compute engine to get automated uncertainty quantification for the output of your AI/ML model predictions. See not just error bars but the full distribution of model output uncertainty.
Technology Explainers
Signaloid's technology provides deterministic uncertainty tracking methods by utilising efficient arithmetic on probability distributions while integrating with existing edge hardware. These technology explainer articles provide further details on how this technology benefits embedded robotics and manufacturing applications, from the underlying mathematics to the engineering implementations.
