De-risk your edge AI development before they reach the field
EdgeLab helps industry teams move from AI development to production-ready edge solutions with confidence. Benchmark and optimise your workloads on real edge hardware, predict field performance, and avoid costly surprises during deployment.
Before hardware decisions are final
Why Edge is hard
Building AI for the edge is fundamentally different from building AI in the cloud. In cloud AI development, teams benefit from standardised infrastructure, elastic resources, and predictable performance. AI workloads can be evaluated, optimised, and scaled without needing to think deeply about the underlying hardware.
At the edge, those assumptions no longer apply. Edge AI development is shaped by its hardware with tight resource constraints and complex interactions between workloads and devices. Understanding how an AI workload will actually behave (and which hardware is truly suitable), is difficult early on, but crucial in making informed decisions.
Fragmented and fast-moving edge hardware landscapes
Reported specifications don't reflect real-world conditions
Unclarity on real-world performance, thermal, memory, power and stability
Late discovery of performance bottlenecks during system integration
Expensive rework when hardware choices turn out to be wrong
How teams deal with edge development today
Buy a small number of devices and hope they scale
Rely on vendor benchmarks and reference designs
Discover constraints late during product development
Compensate by over-specifying hardware
These approaches reduce uncertainty in isolation but don't provide objective, workload-specific evidence across hardware options.
EdgeLab: predictable edge AI, from lab to deployment
EdgeLab provides these benefits by running your AI workloads on a controlled fleet of real edge devices, operated and maintained as neutral infrastructure. This eliminates the need for early hardware commitment.
De-risk hardware selection
Compare your own workloads across state-of-the-art edge devices. Make hardware decisions based on objective, comparable evidence rather than assumptions.
Reduce total cost of ownership
Reduce hidden costs across the lifecycle by avoiding over-engineering, late rework, or failed hardware assumptions.
Predict real-world performance
Measure latency, throughput, stability, thermals and memory usage under realistic conditions.
Faster path from prototype to deployment
Identify performance bottlenecks early, when they are still easy to solve. When ready, migrate your EdgeLab config with zero effort.
Vendor-neutral and trusted
EdgeLab is an initiative by imec. Results are transparent, reproducible and vendor-agnostic.
EdgeLab in practice
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Explore performance of workloads across multiple device classes
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Narrow down to minimal viable hardware options
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Optimize AI workloads using device-specific insights
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Validate assumptions before system integration or deployment
Teams return to EdgeLab as hardware options evolve or workloads change.
Evaluate your workload on edge hardware