  As the use of AI-powered applications widens across multiple domains, so do
increase the computational demands. Primary driver of AI technology are the
deep neural networks (DNNs). When focusing either on cloud-based systems that
serve multiple AI queries from different users each with their own DNN model,
or on mobile robots and smartphones employing pipelines of various models or
parallel DNNs for the concurrent processing of multi-modal data, the next
generation of AI systems will have multi-DNN workloads at their core.
Large-scale deployment of AI services and integration across mobile and
embedded systems require additional breakthroughs in the computer architecture
front, with processors that can maintain high performance as the number of DNNs
increases while meeting the quality-of-service requirements, giving rise to the
topic of multi-DNN accelerator design.
