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Optimizing Heterogeneous Computing at Edge AI

Performing AI workloads at the edge alleviates cost, security, and latency concerns associated with transferring data to the cloud for processing. However, determining IT infrastructure for edge AI applications can be a costly, time-consuming and laborious experimental process. It is unsurprising that implementation is the most-cited challenge by early AI adopters, according to Deloitte State of AI in the Enterprise, 2nd Edition. Learn how to maximize the innovation and productivity gains from AI with ease.

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Increasing Embedded Application Performance with GPUs
Heterogeneous Computing for Artificial Intelligence at the Edge
Optimizing Heterogeneous Computing