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Edge AI
Training and inference on the industrial edge.
Industry Insight. Technology Know-how.
The Benefits of Edge AI for Industry
The ability to bring training and inference right to the edge is creating operational benefits in a wide variety of industries, reducing costs, improving data security, and increasing efficiency. Cloud-based AI has the horsepower to process vast amounts of data, but when advances in GPU technology are combined with rugged computing hardware, Industrial AIOT applications can be deployed right at the edge. Impulse offers rugged Edge AI compute platforms suitable for installation in wide-ranging environments from in-vehicle, industrial, medical, surveillance and drones, with the ability to test your code on our GPU computing systems before committing to the project hardware.
Optimised for AI applications, with wide operating temperatures and fanless designs.
Equipped with extreme GPU performance, wide voltage input and Trusted Platform Module (TPM).
Built for high performance computing, with expansion and VMWare capabilities.
MIC-743-AT Series
NVIDIA Jetson AGX Thor T5000 AI Inference System with 14-Core Arm Neoverse CPU and Wide -10°C to 35°C Temperature Operation.
With a wide range of options, choosing the right AI technology can be challenging. Our Technical Sales team face these challenges head-on, combining industry insight, technology know-how, and remote benchmarking services to ensure you choose the best hardware, suitable for your application.
Not sure what processing unit to choose? Here's an overview of the various types available.
Edge AI Computing For Your Application: Where Our Solutions Work
Rail
Edge AI is used trackside and onboard to detect anomalies from video and sensor streams, such as equipment overheating, obstruction risks, pantograph/contact issues, and condition trends on rotating assets. Running inference locally supports low-latency alerts and reduces the need to backhaul high-bandwidth data from moving trains or remote infrastructure. Outputs typically feed maintenance systems and operational dashboards rather than raw video storage.VIEW OUR RAIL SOLUTIONS
Transport & Fleet Operations
In vehicles and depots, edge AI can process video and telemetry for driver assistance, incident detection, load monitoring, and predictive maintenance signals. Local processing is important because bandwidth is constrained and decisions often need to be made immediately, such as identifying unsafe events or anomalies. Edge systems also simplify data governance by filtering and summarising information before anything is forwarded centrally.VIEW OUR E-MARK SOLUTIONS
Energy & Utilities
Utilities use edge AI at sites such as substations, generation assets, and remote enclosures to detect abnormal equipment behaviour from vibration, thermal, acoustic, or electrical measurements. Keeping analytics on-site supports fast fault indication and reduces the need to backhaul raw data, which is often impractical from remote locations. The output is usually actionable maintenance signals, trend reports, or alarms that integrate into existing SCADA and asset management workflows.VIEW OUR POWER & ENERGY SOLUTIONS
Defence & Military
In deployed environments, edge AI is used for sensor fusion, target or object detection, perimeter monitoring, and anomaly detection where communications are constrained or contested. Processing close to the sensors reduces latency and bandwidth requirements, and allows mission systems to continue functioning when disconnected. Deployments typically emphasise deterministic behaviour, auditing, and strict control over what data is retained or transmitted.VIEW OUR DEFENCE SOLUTIONS
Need to Test Your Code?
Deploy your code onto our Remote GPU Benchmarking systems before committing to project hardware.
Explore Our Edge AI Computing Range
Compact, power-efficient platforms for deploying AI at the edge with NVIDIA Jetson. Ideal for vision, robotics, and real-time inference in space- and power-constrained installs.
Industrial-grade systems built to run demanding AI and vision workloads in harsh environments. Get the acceleration you need with the durability your deployment requires.
High-capacity edge compute for aggregating data, running models, and managing workloads close to the source. Designed for reliable uptime, scalability, and secure deployment.
Add GPU or accelerator power to boost AI, analytics, and visual compute performance. A flexible way to scale capabilities without replacing your whole system.
Explore our recent product highlights, case studies and technical articles.
Our work on industrial projects usually follows a defined process. We start by understanding the environment and application in detail, then agree a suitable hardware platform, build and validate systems in the UK, and finally manage changes and lifecycle.
Projects typically start with a requirements capture phase. This covers environmental conditions such as temperature range, dust levels, vibration and cleaning regime; electrical considerations such as supply, earthing and panel layout; performance requirements; I/O and expansion needs; relevant standards or approvals; and expectations around lifecycle, volume and support.
Based on this information, suitable hardware platforms are shortlisted and reviewed. CPU class, memory, storage, I/O, expansion options and mounting arrangements are agreed, along with any specific BIOS or firmware requirements. For repeat deployments, a small number of standard builds are usually defined to simplify stocking, rollout and support.
Systems are then assembled and validated in the UK. This includes physical assembly, OS installation and configuration, loading of customer images where required, and burn-in and functional tests defined for the project. The goal is that systems arrive on site ready to be integrated with minimal additional work.
Once deployed, attention shifts to lifecycle support. Agreed configurations are documented and controlled as a bill of materials. Changes announced by manufacturers are monitored, and when they occur, suitable replacement options are identified and discussed. Where appropriate, last-time-buy planning and controlled transitions are used to minimise disruption to machines and production lines.
Ready to find out how we can help you?
Contact Us to Discuss Your Project
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GPU computer custom built for autonomous vehicle control system
WMG, University of Warwick’s Intelligent Vehicles research group works with a variety of industrial partners to address challenges presented by the concept of autonomous vehicles. Operating within this department is a multi-million pound funded project ‘Cloud assisted real time methods for autonomy’ (CARMA), working in collaboration with Jaguar Land Rover and EPSRC (Engineering and Physical Sciences Research Council).