Edge AI Platforms for Embedded Systems Design
Platforms for embedded computing enable engineers to deploy machine learning and inference capabilities directly at the edge, where low latency, power efficiency, and deterministic performance are critical. These platforms combine specialised hardware acceleration with software frameworks designed for real-time, on-device intelligence in space, power, and cost-constrained systems.
Our portfolio of embedded AI platforms supports a wide range of applications, from computer vision and sensor fusion to predictive maintenance, human–machine interfaces, and autonomous or semi-autonomous systems. Solutions span microcontroller-based AI and edge SoCs, enabling scalable designs across industrial, medical, transportation, and smart infrastructure applications.
Engineers can evaluate platforms based on key design requirements such as compute performance (TOPS), power consumption, supported neural network models, toolchain maturity, and integration with existing embedded workflows. Support for popular AI frameworks, hardware abstraction layers, and long-term availability ensures faster development cycles and reliable deployment in production environments.
Explore our embedded AI platforms to identify the right balance of performance, efficiency, and ecosystem support for your next edge intelligence design.
