Core Viewpoint - The article discusses the advancements in Li Auto's self-developed chip architecture, particularly focusing on the VLA architecture and its implications for autonomous driving capabilities [1][2]. Group 1: Chip Development and Architecture - Li Auto's self-developed chip is designed with a data flow architecture that emphasizes hardware-software co-design, making it suitable for running large neural networks efficiently [5][9]. - The chip is expected to achieve 2x performance compared to leading chips when running large language models like GPT and 3x for vision models like CNN [5][8]. - The development timeline from project initiation to vehicle deployment is approximately three years, indicating a rapid pace compared to similar projects [5][8]. Group 2: Challenges and Innovations - Achieving real-time inference on the vehicle's chip is a significant challenge, with efforts focused on optimizing performance through various engineering techniques [3][4]. - Li Auto is implementing innovative parallel decoding methods to enhance the efficiency of action token inference, which is crucial for autonomous driving [4]. - The integration of CPU, GPU, and NPU in the Thor chip aims to improve versatility and performance in processing large amounts of data, which is essential for autonomous driving applications [3][6]. Group 3: Future Outlook - The company expresses strong confidence in its innovative architecture and full-stack development capabilities, which are expected to become key differentiators in the future [7][10]. - The relationship between increased computing power and improved performance in advanced driver-assistance systems (ADAS) is highlighted, suggesting a predictable enhancement in capabilities as technology evolves [6][9].
理想自动驾驶芯片最核心的是数据流架构与软硬件协同设计