Background of the solution:
With the development of artificial intelligence, automotive intelligence is becoming a trend, and driver assistance functions are increasing. These functions rely on data from sensors such as cameras and radar, with video processing requiring extensive parallel computing. Traditional CPUs lack sufficient computing power, and driver assistance algorithms require long-term, complex training and optimization.
Solution features:
It provides an integrated setup from the underlying infrastructure to the upper-level deep learning development environment. Through a one-stop AI+HPC unified scheduling and management platform, it realizes the entire cluster deployment, management, monitoring, AI model development and deployment, and HPC job scheduling functions in a visual way, allowing users to efficiently collect, store, simulate and analyze driving data, providing powerful platform support for the development of autonomous vehicles.
Solution Architecture:
