Signaloid

Signaloid provides a computing platform that tracks data uncertainties, dynamically and throughout computations in execution workloads. Our computing platform uses deterministic computations on probability distributions associated with all in-processor state to enable orders of magnitude speedup and orders of magnitude lower implementation cost for computing tasks that are traditionally solved using Monte Carlo methods. The platform is primarily available as a cloud-based computing engine that lets you dynamically compile and run small or large computing tasks via a cloud-based task execution API. We also provide on-premises and edge-hardware implementations of our computing platform.Our platform is the most cost-effective and fastest way to implement uncertainty quantification and is at the same time also the fastest way to perform uncertainty quantification in key use cases, often an order of magnitude or more faster than even the most advanced Monte Carlo methods.Our computing platform is used in applications including materials modeling, autonomous systems, computational finance, machine learning, and noisy intermediate-scale quantum computing. Our team consists of contrarian engineers with combined research, engineering, and leadership experience from Apple, ARC, ARM, Bell Labs, CMU, University of Cambridge, IBM Research, MIT, NEC Labs, and University of Oxford. Find out more at and try out the Signaloid uncertainty-tracking computing platform by signing up for free at https://get.signaloid.io.