78ms的VLA推理！浪潮信息开源自驾加速计算框架，大幅降低推理时延

Core Viewpoint - The article discusses the advancements in autonomous driving technology, particularly focusing on the Vision-Language-Action (VLA) model, which integrates visual perception, semantic understanding, and logical decision-making to enhance the capabilities of autonomous vehicles. The introduction of the AutoDRRT 3.0 framework aims to address the challenges of real-time processing and system optimization for VLA models in automotive applications [2][3][8]. Summary by Sections VLA Model and Challenges - The VLA model is becoming the preferred solution for autonomous driving, enabling vehicles to understand and reason like humans. However, the model's parameter scale has increased to billions, leading to processing delays exceeding 100ms, necessitating optimization of hardware and software systems for real-time performance [2][5][6]. AutoDRRT 3.0 Framework - The AutoDRRT 3.0 framework, developed by Inspur Information, is an open-source solution designed to accelerate the deployment of VLA models in vehicles. It reduces the end-to-end latency of VLA models from 8000ms to 78ms, achieving a performance improvement of 102 times [3][13][23]. Innovations in Computation - AutoDRRT 3.0 introduces several computational innovations, including parallel decoding, visual pruning, and operator fusion. These techniques significantly enhance the efficiency of the VLA model's inference process, allowing for smoother and faster action outputs [9][12][13]. Communication Mechanism - The framework also features a high-performance communication mechanism that optimizes data transfer between heterogeneous computing units, reducing latency and improving the overall responsiveness of the system. This mechanism allows for zero-copy data transfer, enhancing efficiency during large data loads [16][17][23]. Scheduling Innovations - AutoDRRT 3.0 implements a unified scheduling framework for heterogeneous computing resources, ensuring efficient task management and resource allocation. This approach minimizes idle computing time and enhances the overall system stability and performance [18][21][20]. Future Prospects - The article concludes that the AutoDRRT 3.0 framework not only validates the feasibility of real-time operation of VLA models in vehicles but also lays a solid foundation for the transition of autonomous driving technology towards scalable and replicable solutions across various applications [23].