The development of AI for embedded/edge computing systems presents unique challenges and requirements that differ from those of traditional server or desktop systems. While servers and desktops can benefit from their powerful hardware, embedded systems must address strict resource constraints, energy efficiency requirements, and specific real-time requirements. The successful implementation of AI on embedded/edge devices therefore requires specialized approaches, optimization techniques, and AI solutions tailored to the specific needs of these systems.
The design of compact AI models that run with high performance on embedded hardware and, depending on the embedded system, also manage with low hardware resources, requires a great deal of experience. This is one of Chimaera's core competencies. We have been developing individualized AI networks consisting of layers since 2012, since the release of the first CNNs such as AlexNet. As a result, we have gained a lot of experience in dealing with various AI technologies and are therefore always up to date with the latest AI architectures.