Edge AI Deployment Beyond Models: A BSP-Aware Systems Framework for Industrial Embedded Platforms
arXiv:2605.26119v1 Announce Type: cross Abstract: Industrial Edge AI programs often begin with the model and only later confront the platform. That sequencing is attractive because it allows early demonstrations, but it breaks down when the deployment target is an embedded system with long product lifecycles, vendor-specific kernels, heterogeneous accelerators, safety constraints, and nontrivial I/O paths. In that environment, a model is only one component of a larger execution chain that begins at the sensor, traverses the board support package (BSP), and ends in a production service loop. Th
The proliferation of AI models is forcing deeper consideration of practical, real-world deployment challenges on existing industrial embedded systems, moving beyond theoretical model development.
This paper highlights the critical but often overlooked complexities of integrating AI into industrial hardware, affecting adoption rates, performance, and the long-term viability of industrial AI solutions.
The focus shifts from merely model development to a more holistic, systems-level approach that considers the entire execution chain from sensor to board support package in industrial AI deployments.
- · Embedded systems developers
- · Industrial automation companies
- · BSP vendors
- · Edge AI platform providers
- · Companies with a 'model-first' deployment strategy
- · Generic AI software providers
- · Legacy industrial hardware manufacturers without AI integration strategies
Industrial AI deployments will become more robust and reliable due to a focus on full-stack integration.
This will accelerate the adoption of AI in critical industrial sectors where embedded systems are prevalent, such as manufacturing and energy.
The specialized expertise required for such deployments could lead to new niche markets for AI engineering services and highly integrated hardware/software solutions.
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Read at arXiv cs.AI