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Abstract
Benchmarks are essential for balancing the benefits and risks of AI by providing quantitative tools that guide responsible development. They offer objective and consistent metrics for accuracy, speed, and efficiency, enabling engineers to develop reliable products and services. Additionally, benchmarks help researchers gain new insights that can drive future innovations. Today, numerous cloud-based AI development services allow software developers, even those without expertise in data science, to utilise AI models through APIs, SDKs, or applications. Benchmarking these models on cloud infrastructure is a feature offered by these services. However, few of these services are designed for edge deployment, where deep expertise in embedded programming and system integration is necessary to optimize and deploy AI models on specific embedded devices. Comparing benchmarking results across different embedded boards becomes increasingly complex when targeting devices from various providers. The current project aims to design and implement a collaborative platform that enables researchers and developers to conduct experiments and research across various edge AI domains and edge AI devices. This will be achieved by sharing resources on a distributed virtual laboratory (dAIEdge-VLab). This platform will provide access to dedicated resources, tools, and services, allowing end users without expertise in embedded programming to perform live AI experiments, such as benchmarking, on remote embedded boards.