论文标题
下一代AI系统的多DNN加速器
Multi-DNN Accelerators for Next-Generation AI Systems
论文作者
论文摘要
随着AI驱动应用程序的使用跨多个域扩大，因此请增加计算需求。 AI技术的主要驱动力是深神经网络（DNNS）。当将要么专注于提供来自不同用户的多个AI用户的基于云的系统时，要么使用自己的DNN型号，或者使用使用各种型号的管道或并行DNN的移动机器人和智能手机，以同时处理多模式数据时，下一代AI系统将具有多模式的多模式系统。在移动和嵌入式系统上进行的AI服务和集成的大规模部署需要在计算机架构方面进行其他突破，并且处理器可以随着DNN的数量增加而在满足服务质量要求的同时，可以保持高性能，从而引起了多DNN ACCELERATOR设计的主题。
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.
