Example Performance Validation
Run your existing quantitative finance, engineering, and robotics or sensor applications on the Signaloid compute platform and leverage our platform's capability to perform arithmetic on probability distributions, with speedups of over 1000× in some cases. The examples on this page highlight representative speedups across these three use case domains.
Quantitative Finance
Achieve 2× to over 1000× speedup while achieving the same degree of convergence, for your options pricing and risk models that you currently implement using Monte Carlo methods. Achieve these speedups with minimal changes to your existing C/C++ code. Give your existing optimized quant libraries an additional boost, with integration with your existing compilers and numerics libraries.
The performance evaluations below provide some examples of the performance, cost, and engineering benefits of adopting the Signaloid compute platform in financial risk and pricing use cases.
Engineering Design and Manufacturing
Achieve 2× to over 2500× 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.
The performance evaluations below provide some examples of the performance, cost, and engineering benefits of adopting the Signaloid compute platform in engineering use cases which today require the use of Monte Carlo methods for uncertainty quantification.
Robotics, Sensors, and Automation
Achieve real-time uncertainty quantification and state-of-the-art state estimation implementations with typical speeds that are 12× to over 600× times faster than Monte Carlo methods when applied in many sensor data processing use cases. Use either the cloud-based deployments of the Signaloid Compute Engine or deploy at the edge of the network with Signaloid's system-on-module hardware.
The performance evaluations below provide some examples of the performance, cost, and engineering benefits of adopting the Signaloid compute platform for uncertainty quantification in use cases that today employ Monte Carlo methods or where uncertainty quantification is not desired but not performed because of the high costs of Monte Carlo methods.
