Easily Implement Stochastic Differential Equations, Bayesian Methods, and More
Easily implement particle and Kalman filters for sensor fusion, Bayesian inference methods in machine learning, numerical solution of stochastic differential equations using Brownian motion processes, uncertainty quantification (UQ) in high-performance engineering simulations, and more. Take advantage of Signaloid's UxHw® technology to replace your implementations of Monte Carlo methods, in these use cases, with deterministic computations on probability distributions; no Monte Carlo loops required. Start with your existing unmodified code in any language supported by the LLVM compiler infrastructure.

Minimal Code Changes
Signaloid's UxHw technology gives your applications the abstraction of running over a virtual processor whose input data can be annotated with probability distributions. The processor performs arithmetic on these distributions as it runs binaries, detecting correlations as they occur.
Our LLVM-based compiler toolsuite lets you compile applications in any of the frontend languages supported by LLVM (e.g., C/C++). When you run your applications over the Signaloid C0 virtual processor:
Three Ways to Integrate:
Cloud-Based Kernel Compilation and Task Execution
Run applications over the UxHw virtualization layer without needing to setup and maintain your own infrastructure. Build on our secure ISO 27001 and SOC2 Type II certified platform, with geographic redundancy and auto-scaling to workloads.
Use our REST APIs to pre-compile codebases, launch compiled tasks, and retrieve results. The Signaloid Cloud Compute Engine (SCCE) SDK eases integration and the signaloid-cli command-line utility even allowing you to build complete UxHw-enhanced web applications, complete with UI elements optimized for interactive input and display of probability distributions.



Best for:
Cloud-native applications
Batch processing
Integration with existing pipelines
Integrate with Existing CPU and GPU Hardware
Deploy the UxHw virtualization layer on your existing hardware. Whether server-based on-premises hardware like AWS Outpost servers, or edge compute hardware like Nvidia Jetson and Nvidia DRIVE, augment your existing hardware and software optimization investments.

Use UxHw virtualization layer as the sole execution environment.

Deploy UxHw-augmented software components side by side with your traditional components.
Best for:
Harness existing CPUs/GPUs
Complement existing optimizations
On-premises/edge deployment
Our Specialized Hardware Modules
Achieve even greater speedups than the default UxHw technology, with additional hardware acceleration.
Augment your on-premises server-based UxHw deployments with our high-end FPGA-based UxHw accelerators built on Xilinx/AMD Virtex Ultrascale FPGAs. No software changes needed to your existing UxHw-enhanced applications that you've previously deployed on our cloud-based and on-premises integration modes.
For low-power edge-of-network deployments, our C0-microSD, C0-microSD+, C0-SD, and C0-M.2 modules allow you to easily integrate UxHw technology into your existing systems, with no hardware modifications necessary. These modules present a mass storage device interface through which you can send UxHw-enhanced applications for execution and retrieve results. Achieve easier implementation and faster execution without needing custom kernel drivers to interface with these hardware modules.

Best for:
Quantitative Finance
Robotics
Industrial automation
Edge computing
Visualization of one example use case (Brownian motion in quantitative finance), implemented with traditional methods (first) compared to running on Signaloid's UxHw technology (second).
Dig Deeper: UxHw Execution Semantics
UxHw technology propagates distribution metadata alongside the non-distributional data when running your binaries, without requiring any explicit changes to your software. You can however explicitly access the distributional information when you need to, such as to retrieve summary statistics from the final algorithm output or to explicitly inject or manipulate distributional data and distributions correlations.
Used by Developers at:
















Configure UxHw To Match Your Use Case
Choose the right UxHw technology variant for your use case:

UxHw Core Type
Description
Best For
C0
High-performance uncertainty tracking
Advanced debugging capabilities
C0-Reference
Monte Carlo equivalent (guaranteed accuracy)
Validation and debugging
C0-Bypass
Standard execution (no uncertainty)
Porting and testing
Quick Start (5 Minutes)
1
Sign Up
Create a free account on Signaloid Cloud Developer Platform.
2
Write Code
Use the in-browser editor or connect your GitHub repository.
3
Run
Click “Compile and Run.” Hover over outputs to see full distributions.
4
Integrate
Use the REST API to integrate into your production systems.
Use Cases
Quantitative Finance
"Our VaR calculations take hours and cost a fortune."
Accelerate risk calculations and derivatives pricing by over 400x.
Digital Banking
"Our clients want interactive multi-scenario modeling, not one-off results from an analyst."
Enable interactive what-if analysis that responds in milliseconds.
Machine Learning and AI
"We need to know when we can trust our AI/ML model predictions."
Add calibrated uncertainty to any ML inference pipeline.
Robotics and Automotive
"We need easier ways to achieve high-performance LiDAR particle filters on our edge GPU hardware."
Simplify state estimation with automatic uncertainty propagation.
Industrial Automation
"We can't trust AI/ML predictions enough to deploy them on the factory floor."
Make AI transparent with real-time confidence quantification.
Engineering Design
"We run thousands of simulations to understand design margins."
Get full uncertainty quantification in a single simulation pass.
Supply Chain Planning
"We need a compute platform to ease implementing range-based and probabilistic forecasting."
Replace fragile point estimates with robust probabilistic planning.