Signaloid Announces Availability of Amazon AWS Machine Image (AMI) for Accelerating Compute Workloads Ranging from Finance to Reinforcement Learning

British computing technology company Signaloid today announced the release of the Signaloid Compute Engine Amazon Machine Image (AMI) via AWS Marketplace. The release enables organizations to deploy Signaloid’s distribution-extended compute hardware (UxHw®) technology within their Amazon Virtual Private Clouds (VPCs).

The AMI provides access to UxHw, which delivers orders-of-magnitude performance improvements on x86_64 and ARM (AArch64) AWS Elastic Compute Cloud (EC2) instances. Without requiring software rewrites, UxHw enables existing applications to compute directly on probability distributions, automating algorithms such as Monte Carlo methods in finance and physics, importance sampling in reinforcement learning, and particle filters in physical AI and robotics. The technology works through binary translation and optimization at the LLVM intermediate representation (LLVM IR) level, with optional hardware acceleration via FPGAs and Signaloid’s C0-ASIC that was recently taped-out in an ultra-low-power TSMC process. Examples of performance achieved with the AMI include 430-fold speedup for Value at Risk (using geometric Brownian motion) and up to 580-fold speedup for Heath-Jarrow-Morton swaptions pricing.

For organizations who currently use AWS infrastructure and want to benefit from UxHw combined with the familiarity of AWS tools, the AMI permits rapid deployment to EC2/On-Premises compute instances to benefit from UxHw. Organizations also have the option to deploy applications to Signaloid’s managed compute infrastructure, which has ISO/IEC 27001:2022 certification and SOC 2 Type II attestation.

The Signaloid Compute Engine AMI is available through the AWS Marketplace.

About Signaloid

Signaloid provides computing platforms for dramatically reducing the runtime and compute infrastructure requirements of many workloads ranging from quantitative finance and engineering simulations, to robotics and physical AI. Unlike conventional CPUs and GPUs, which use large amounts of sheer compute force across thousands of compute cores, to solve problems that require iterative randomized variations, Signaloid’s UxHw builds on new mathematical techniques to restructure computations, dynamically, to achieve the same results while often using 1000-fold less energy. With a range of deployment options including a cloud compute platform, on-premises deployment, and edge hardware, Signaloid's technology is designed for easy adoption and integration into an organization's information technology infrastructure. For more information about Signaloid, visit https://signaloid.com or visit https://signaloid.com/introduction for a 3-minute introduction to its technology.

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