5. Heterogeneous computing _ "3, AI algorithm optimization"

It refers to using different types of processors to handle diverse AI workloads to improve overall computing efficiency. This approach combines multiple computing units such as CPU, GPU, FPGA, etc. to select the best processor based on task requirements.

Process: When executing AI tasks, the system dynamically selects the most suitable processor for calculation based on the nature of the workload. This selection can be based on real-time performance monitoring and load balancing strategies to optimize overall efficiency.

Application: Heterogeneous computing performs particularly well in scenarios that require high computing power and flexibility, such as deep learning training, real-time image processing, and large-scale data analysis. It can maximize resource utilization while reducing computing costs.