Enhance Your PLCs With Explainable AI/ML
Transform your factory floor AI/ML with quantified uncertainty without infrastracture overhaul needed.
Reduce factory downtime. Improve part quality. Cut waste.
These aren't just goals, they're measurable outcomes when you can quantify uncertainty in real-time factory floor data analyses.
Trust your industrial models
Whether using bespoke algorithms or AI/ML models, Signaloid's UxHw-enhanced compute modules reveal how data properties affect output reliability, making predictions explainable and trustworthy.
Orders of magnitude faster than Monte Carlo
Deploy high-performance uncertainty quantification orders of magnitude faster than alternatives that works with real-time data feeds, improving production line quality, safety, and costs.
Integration made simple
Multiple module variants can plug into unused microSD slots, interfacing through standard SD block storage operations.
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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 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.
