In the current era of AI and Big Data,an increasing and significant amount of computing power is needed for many applications and algorithms such as AIGC models,face detection,autonomous driving and atmosphere simulat...In the current era of AI and Big Data,an increasing and significant amount of computing power is needed for many applications and algorithms such as AIGC models,face detection,autonomous driving and atmosphere simulation.Recently,there is a significant amount of interest among the community in improving AI and big data applications with heterogenous computing,which refers to a computing system using different types of computing cores such as GPU,NPU,ASIC,DSP and FPGA.It can improve the performane and enery efficiency by dispatching different workloads to processors that are designed for specialized processing and specific purposes.This issue aims to cover challenges that can hamper efficiency and utilization for AI and big data applications on heterogenous computing systems,such as efficient utilization of the raw hardware,I/O management,task scheduling,etc.展开更多
文摘In the current era of AI and Big Data,an increasing and significant amount of computing power is needed for many applications and algorithms such as AIGC models,face detection,autonomous driving and atmosphere simulation.Recently,there is a significant amount of interest among the community in improving AI and big data applications with heterogenous computing,which refers to a computing system using different types of computing cores such as GPU,NPU,ASIC,DSP and FPGA.It can improve the performane and enery efficiency by dispatching different workloads to processors that are designed for specialized processing and specific purposes.This issue aims to cover challenges that can hamper efficiency and utilization for AI and big data applications on heterogenous computing systems,such as efficient utilization of the raw hardware,I/O management,task scheduling,etc.
