Uncertainty quantification (UQ) is a standard part of all high-stakes engineering design processes, allowing engineers to understand how uncertainties in parameters or inputs to an engineered system affect the system's behavior. Examples range from statistical timing analysis and variability analysis in chip design, to tire and engine simulations in motorsports. The trusted approach for achieving UQ is often to use Monte Carlo methods. By running your engineering workloads on the Signaloid Cloud Compute Engine, you can achieve much higher quality UQ than Monte Carlo methods while using the same computing resources, or speedup your UQ analysis while maintaining the same level of quality.
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Solutions
Choose how to integrate Signaloid’s UxHw into your technology, with deployment methods including our cloud-based compute engine, or custom hardware modules such as microSD-sized hot-swappable compute-in-memory modules.
Example Performance Validation
Achieve 2x to over 300x 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.
