Signaloid to Preview New ASIC and Demo of Its UxHw® Technology at Bosch Connected World
Signaloid to Preview New ASIC and Demo of Its UxHw® Technology at Bosch Connected World
Publish Date: 2026-06-09 01:00:00
Source Domain: finance.yahoo.com
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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.
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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, England, June 09, 2026–(BUSINESS WIRE)–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,…
