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British compute hardware company Signaloid has joined the Open Chiplet Alliance (OCA) and announced plans to make its UxHw® compute acceleration technology available as chiplets within the OCA ecosystem. The UxHw technology targets AI and simulation workloads that rely on stochastic methods, including quantitative finance, reinforcement learning, engineering simulations, and world models. The announcement follows Signaloid’s recent tapeout of a UxHw ASIC for robotics and physical AI in an ultra-low-power TSMC process. “The Open Chiplet Atlas (OCA) increases innovation in chip design by defining an open architecture for multi-vendor chiplet interoperability. In doing so, it enables new Systems-in-Package (SiPs) with reduced non-recurring engineering (NRE) costs and a vastly expedited time-to-market,” says Wei-han Lien, Chief CPU Architect and Senior Fellow of Tenstorrent. “We are thrilled to welcome Signaloid to the OCA ecosystem. Their unique, innovative accelerator for stochastic workloads adds a highly specialized capability that enriches our expanding suite of chiplets.” A Different Kind of AI Accelerator Chiplet Signaloid’s UxHw technology delivers orders-of-magnitude speedups for workloads common in robotics, machine learning, quantitative finance, and engineering simulations. These workloads often rely on iterative algorithms with randomized variations, including Monte Carlo methods, importance sampling, and particle filters, to evaluate large numbers of possible scenarios in searching for a solution. Unlike conventional CPUs and GPUs, which handle such computations through repeated execution across many compute cores, Signaloid’s UxHw dynamically restructures computations to process information about probable outcomes directly and more efficiently. In competitive benchmarking against contemporary high-end server processors, UxHw has demonstrated speedups of multiple orders of magnitude while often reducing energy consumption by up to 1000×. What the Chiplet Will Enable Signaloid’s existing binary-translation-based cloud instances, FPGA implementations, and ASIC realizations of UxHw already provide multiple orders-of-magnitude speedups over conventional approaches for stochastic workloads. By bringing UxHw to the OCA ecosystem as a chiplet, Signaloid aims to enable tighter integration with next-generation heterogeneous AI accelerators. The UxHw technology and its implementation are covered by a growing portfolio of more than 90 intellectual property filings across the US, China, Taiwan, Japan, and the EU.

Cambridge, UK, 14th September 2026 — British technology company Signaloid today announced the appointment of three distinguished industry veterans to its advisory board and senior leadership roles, to strengthen the commercialization momentum behind its technology: Dr. Han Lee, former Global Head of Quantitative Strategies and Automated Trading for the Fixed Income Division at Morgan Stanley, Dr. Nachiketh Potlapally, former Architect at Oracle Cloud Infrastructure, and Christian Roth, former Senior Director Enterprise Sales, Intel. Bringing deep expertise in quantitative finance and cloud infrastructure, the appointments build on Signaloid’s commercial traction momentum in Europe, Japan, and the United States. Dr. Lee, prior to his executive role at Morgan Stanley, served as Global Head of Quantitative Analytics at RBS and holds a Ph.D. in Theoretical Physics from the University of Cambridge. He is a co-founder of RLxPartners Ltd, and serves as an advisor to the Head of the Department of Physics at the University of Oxford. Dr. Potlapally holds a Ph.D. in Computer Science from Princeton University. He previously worked at Intel on the architectural security of high-performance processors and servers, was a security architect  at Amazon Web Services (AWS) Oracle Cloud Infrastructure (OCI). As an early member at both AWS and OCI teams, he worked on building secure cloud infrastructure from ground-up.  Mr. Roth, who is joining Signaloid as Chief Commercialization Officer, joins Signaloid from over two decades at Intel where he served in various leadership positions in Sales including Senior Director Enterprise Sales and Global Key Account Director. In addition he served as the Director of Product Marketing in EMEA for Data Center, Business Clients, Storage and Networking Platforms. He is an Insead Alumnus (IEP), holds an MBA from the Carlsson School of Management/University of Minnesota, USA and a Global EMBA from the Executive Academy of the WU Vienna, Austria (Wirtschaftsuniversität Wien).    Signaloid’s UxHw Technology: From Server-Based Quantitative Finance to Edge Robotics and Physical AI Many mission-critical applications ranging from financial risk modeling and energy safety analysis to chip design, robotics, and AI , rely on stochastic methods such as Kalman filters, particle filters, importance sampling, and Monte Carlo simulations. These methods are particularly important in modern AI and machine learning (ML), powering applications ranging from reinforcement learning and probabilistic programming to localization and decision-making in physical AI systems. Traditional CPUs and GPUs typically execute such workloads through repeated calculations across thousands of cores, often requiring substantial compute resources, runtime, and energy. Signaloid’s distribution-enhanced compute hardware (UxHw®) takes a different approach to running stochastic workloads. Instead of relying on large numbers of repeated calculations, UxHw restructures computations, using binary translation optionally augmented with hardware acceleration, to work directly on probability distributions. This enables results that can otherwise require billions of operations on conventional hardware. Deployed on existing hardware, UxHw can deliver performance improvements of several orders of magnitude, in some cases on the order of 1,000x, without requiring software rewrites, at the same time reducing energy consumption by similar orders of magnitude. On AWS r7iz compute instances, UxHw has demonstrated substantial performance gains across quantitative finance workloads, including up to 430x acceleration for Value at Risk (VaR) calculations using geometric Brownian motion and up to 580x acceleration for computations involving Heath–Jarrow–Morton models (typically calibrated to a grid of swaption prices). In robotics and physical AI, UxHw has delivered more than 37x speedups for particle filter algorithms on embedded microcontroller units (MCUs). Additional gains are possible when UxHw is combined with Signaloid’s recently announced UxHw C0-ASIC. What the new advisors bring to Signaloid: Buyer-Side Experience from Quantitative Finance and Enterprise Clouds The appointment of the new advisors will help Signaloid build on its recent momentum with the availability of its UxHw toolchain for deployment on both cloud-based and on-premises Amazon Web Services (AWS) instances for quantitative finance, engineering simulation, and high-energy physics simulations, as well as new distributions channels for its edge hardware modules (with distribution contracts in place with Mouser Inc. and DigiKey Inc., two of the world's largest semiconductor device distributors).  In this context, newly-appointed advisory board chair, Dr. Han Lee, former Global Head of Quantitative Strategies and Automated Trading at Morgan Stanley, says “It is not often that you find a new computing technology that already solves commercially-important challenges today, and which also has the potential to be the basis for the future of computation. Signaloid have not only invented a new class of computing platform, but have also gone all the way to engineering it into a production-ready cloud compute platform that has received the major security certifications demanded by enterprise users. As a physicist, I find it impressive to see their technology being applied already in deployments from Boeing, to Bosch, to CERN, and at the same time achieving order-of-magnitude speedups over high-end server processors on quantitative finance workloads. I am delighted to join Signaloid as an advisor.”

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.

British AI hardware company Signaloid will preview its recently-taped-out ASIC for physical AI at Bosch Connected World, from 10th–11th June 2026, in Berlin. The ASIC is complementary to Signaloid’s edge hardware modules which are already achieving over 37-fold speedup for algorithms used in physical AI and robotics. Cambridge UK, 9th June 2026 — British computing technology company Signaloid will preview its C0-ASIC for physical AI this week at Bosch Connected World, taking place from 10th-11th June, in Berlin. Designed for robotics, industrial automation, and probabilistic AI workloads, the ASIC is projected to deliver up to 1000× better performance-per-Watt than existing state-of-the-art approaches. Signaloid’s distribution-extended compute hardware (UxHw®) is already available for use in physical AI/robotics as a family of hardware modules, as well as via a virtualization- and binary-translation-based solution. UxHw enables autonomous mobile robots (AMRs) to improve their navigation algorithms for safer and faster navigation in factories. It similarly enables industrial programmable logic controllers (PLCs) to achieve better predictive maintenance. Why Physical AI and robotics needs different compute Many of the important algorithms enabling robotics and AI today require compute-intensive GPUs or similar hardware. They often involve algorithms that must evaluate hundreds of thousands or even millions of possible scenarios each second, from estimating a robot’s position to tracking a drone in space. Because these scenarios are not equally likely, today’s processors rely on repeated computations to approximate the ideal solutions. If AI hardware could however consider all the possible scenarios when handling any single value, that could enable everything from more efficient AI datacenters to more agile robots and safer autonomous mobility. A new kind of compute hardware Instead of single numbers, UxHw can represent values as arbitrary non-uniform ranges (i.e., probability distributions) and performs computation directly on this digital form, without requiring significant software changes. A single execution of traditional software on a UxHw-enabled computing platform can therefore deliver what classical iteration-based methods need millions of repetitions to approximate. In competitive benchmarking against the latest high-end computing platforms, UxHw already delivers 1000-fold speedups, with further gains expected from Signaloid’s C0-ASIC. What the ASIC will enable “The compute workloads that Signaloid’s UxHw is designed for, are used across many aspects of computing, from physical AI and robotics, to supply-chain modeling, logistics, and quantitative finance”, says Phillip Stanley-Marbell, founder and CEO of Signaloid. Even before availability of the C0-ASIC, cloud- and FPGA-based implementations of Signaloid’s UxHw are already demonstrating speedups of over 600-fold for infrared sensor data analysis and over 37-fold for particle filter sensor fusion algorithms. The C0-ASIC will complement Signaloid’s existing hardware modules, which are available for use with a range of industrial applications including for integration with the Bosch Rexroth’s ctrlX core X2 and core X3 PLCs.