Edge AI embedded software
AI frameworks and runtimes for on-device vision processing
Edge AI embedded software provides the intelligence behind embedded imaging systems, allowing vision data to be analysed directly where it is captured. By processing data locally, it can enable real-time decision-making without relying on a continuous connection to the cloud or a remote server.
Rather than being tied to a specific piece of hardware, this category focuses on the software frameworks, inference engines and development tools that enable AI models to run efficiently on embedded platforms. The right software can turn available processing power and imaging data into practical capabilities such as object detection, classification, tracking and segmentation.
For engineers, selecting the right solution means looking at more than just the AI model. Framework compatibility, hardware acceleration, inference performance, deployment, updates and long-term maintainability all need to be considered together.
While embedded computers provide the processing resources and smart cameras combine imaging and compute in a single platform, the software layer determines how those resources are used and what the system can ultimately do.
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Why our portfolio is right for you
Our Edge AI embedded software solutions help bring real-time intelligence directly to your imaging system, allowing vision data to be processed where it is captured. This can reduce latency, limit the need to send large volumes of data to remote infrastructure and enable more responsive, self-contained applications.
We support a range of AI frameworks, inference environments and embedded platforms, helping you select an approach that fits your sensors, compute hardware, AI models and application requirements. From initial model deployment through to optimisation and system integration, our focus is on making AI practical within the constraints of an embedded imaging system.
Whether you’re developing a smart camera, building a modular edge-AI platform or adding vision intelligence to an existing system, our technical expertise can help you navigate the software and hardware dependencies. We work with you to balance model performance, processing requirements, latency and long-term maintainability, helping you move from an AI model to a reliable, deployable imaging solution.
Key selection factors
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Framework compatibility: The software needs to support your chosen AI frameworks, models and target hardware. Checking compatibility early can avoid significant development work later in the project.
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Model optimisation and performance: The best model is not necessarily the largest or most accurate one. Quantisation, pruning and hardware-specific optimisation can help achieve the right balance between accuracy, speed, memory use and power consumption.
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Deployment and update strategy: Consider how models and software will be installed, updated and monitored once systems are deployed. This becomes particularly important when managing multiple or distributed devices.
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Hardware abstraction vs control: Higher-level software can make development faster and easier, while lower-level access can provide greater control over hardware resources and optimisation. The right balance depends on the application and development requirements.
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Scalability across platforms: Software that can support different embedded platforms, from smart cameras to modular embedded computers, gives you more flexibility as your system develops.
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Latency and resource usage: Real-time vision applications depend on efficient processing. Inference time, memory usage, processor load and power consumption should all be considered when evaluating the software stack.
Technical overview
Edge AI embedded software encompasses the frameworks, inference runtimes and development toolchains used to deploy computer vision models on embedded hardware. These solutions can support a range of applications, including detection, classification, segmentation, tracking and other forms of real-time image analysis.
The software needs to work effectively with the underlying processor, which may include a CPU, GPU, NPU or other dedicated AI accelerator. Model optimisation is therefore an important part of deployment. Techniques such as quantisation, pruning and hardware-specific optimisation can help reduce processing requirements while maintaining the required level of accuracy.
A typical workflow involves taking a trained AI model, converting it into a format supported by the target hardware and optimising it for the chosen inference runtime. The software then becomes part of the wider imaging pipeline, handling tasks such as image acquisition, pre-processing, inference and post-processing.
This makes the software layer an important part of overall system performance. Efficient implementation can reduce latency and power consumption, while a well-structured software architecture can make models easier to update, maintain and move between supported platforms.
Integration notes
Edge AI software sits between the imaging hardware and the application, so successful integration depends on how effectively these different layers work together. The software needs to receive image data from the sensor, move it efficiently through the processing pipeline and deliver the resulting AI output to the wider system.
The compute platform and available accelerators should be considered from the beginning, as hardware-specific optimisation can have a significant impact on performance. A capable processor alone does not guarantee fast inference if the model, runtime or data pipeline has not been configured effectively.
Smart cameras can simplify integration by providing much of the software stack as part of a pre-integrated platform. Modular embedded systems offer greater flexibility, but typically give engineers more responsibility for configuring the operating environment, runtime, model deployment and data flow.
Resource allocation is another important consideration. Image capture, pre-processing, AI inference, post-processing and communications may all compete for the same compute and memory resources, particularly on compact embedded platforms.
Finally, software lifecycle management should be considered as part of the system design. The ability to deploy updates, replace models, monitor performance and maintain compatibility over time can be just as important as achieving the required inference performance at initial deployment.
FAQ’s
Engineers need to know whether the software supports their target SoC, CPU, GPU, NPU, FPGA, or accelerator. Compatibility should include specific processor families, operating systems, drivers, and board configurations.
Check support for frameworks such as PyTorch, TensorFlow, ONNX, and vendor-specific runtimes. Model-format compatibility can significantly affect how easily existing models can be deployed.
The deployment workflow should cover model conversion, quantisation, compilation, optimisation, runtime integration, and packaging. A well-defined toolchain can substantially reduce engineering effort.
Look beyond TOPS or theoretical throughput. Relevant metrics include inference latency, FPS, memory usage, CPU utilisation, power consumption, and performance under the actual workload and camera resolution.
Determine whether inference can use the device’s GPU, NPU, DSP, FPGA, or other accelerator. Efficient use of hardware acceleration is often critical for achieving real-time performance within a power budget.
Useful capabilities may include profiling, performance analysis, logging, model inspection, hardware utilisation monitoring, and tools for identifying bottlenecks.