The growing number of transportable AI applications, from autonomous driving to object identification in military aircraft, increases the need for users and data scientists to focus on overall AI system architecture design rather than individual innovations in a single hardware component or piece of software.
Where a traditional multi-rack datacenter may be able to integrate the latest individual enhancement in AI framework, processing, networking, flash storage or GPU, an integrated embedded AI system in a mobile environment requires a complete system change to take advantage of these innovations.
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Today’s AI computer architectures - relying on switched fabrics - need new packaging approaches that can handle the demanding requirements of military applications in the field. But program leaders face the dilemma of meeting these requirements using open standards that were never initially designed for them.
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