Engineering, Manufacturing,
and Robotics
Signaloid’s computing platform lets you replace your existing engineering workloads that use Monte Carlo methods and to achieve speedups as high as 10x to 1000x in relevant use cases. Examples in the engineering design, manufacturing, and robotics domain range from speeding up Monte Carlo analysis in circuit simulation, easier implementation and higher-fidelity uncertainty quantification in engineering design, and higher-performance state estimation algorithms.
Solutions
Choose between deployment models ranging from high-performance PCIe M.2 m-key accelerator cards, to cloud-based compute infrastructure, to cost-optimized hot-swappable compute-in-memory modules integrated into microSD and full-SD storage devices.
Example Performance Benchmarks
Achieve 2x to over 300x speedups in your uncertainty quantification of existing C, C++, and FORTRAN models currently implemented using Monte Carlo methods. 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 over the Signaloid compute engine to get automated uncertainty quantification for the output of your AI/ML model predictions. See not just error bars but rather the full distribution of model output uncertainty.
Technology Explainers
Compatibility with your existing edge hardware, efficient digital representations of probability distributions, and efficient arithmetic on probability distributions. The technology explainers below provide more details on how Signaloid's technology works, in the context of embedded/edge, robotics, and manufacturing/PLC applications, from the underlying mathematics to the engineering implementation and applications.
