Core Viewpoint - The article discusses the evolution of intelligent driving algorithms and the importance of data flow architecture in the context of autonomous driving technology, emphasizing the need for advanced computational architectures to handle increasing demands for processing power and reasoning capabilities. Group 1: Evolution of Intelligent Driving Algorithms - The evolution of autonomous driving algorithms can be divided into three phases: the initial phase relied on rule-based algorithms, the second phase shifted towards end-to-end (E2E) learning, and the current phase is focusing on integrating visual language models (VLM) with reinforcement learning (RL) to enhance decision-making capabilities [4][5][6]. Group 2: Importance of Language Models - Language models are deemed essential for achieving long reasoning capabilities in autonomous driving, as they enable the system to generalize and handle corner cases that cannot be addressed solely through data collection or world models [7][8]. - The psychological aspect of having a driving model that aligns with human values and reasoning is highlighted, suggesting that language models can help instill a human-like worldview in autonomous systems [8][9]. Group 3: Computational Architecture - The article critiques the traditional von Neumann architecture, which prioritizes computation over data, and proposes a shift towards data-driven computation to better handle the complexities of AI processing [12][13]. - The company has developed a unique NPU architecture that focuses on data flow rather than traditional SOC designs, aiming to improve efficiency and performance in AI inference tasks [17][18]. Group 4: Performance Metrics - The performance of the company's NPU architecture is reported to be significantly higher than existing solutions, achieving up to 4.4 times the performance in CNN tasks and 2 to 3 times in LlaMA2 7B tasks, while maintaining similar transistor counts [2][18].

Group 1 - The global AI chip market is witnessing a shift towards data flow architecture, with companies like SambaNova and Groq achieving significant valuations of $5 billion and $6 billion respectively [1] - Clear Microelectronics, originating from Tsinghua University, has successfully developed and mass-produced data flow reconfigurable chip technology, positioning itself as a leader in this emerging field [1][2] - The founder of Clear Microelectronics, Wang Bo, emphasizes the need for innovation beyond traditional GPU architectures to overcome limitations in technology and materials, advocating for a "leapfrog" approach similar to the automotive industry's transition to electric vehicles [2] Group 2 - Clear Microelectronics' first "new computing power" chip, TX81, has achieved over 20,000 orders and established intelligent computing centers across multiple regions in China within just six months of its launch [2] - Investment institutions are increasingly recognizing the value of new computing power, with significant investments from major funds indicating a strong market trend towards data flow architecture [3] - The transition to data flow architecture is seen as a critical signal for achieving self-sufficiency in the computing power industry, with support from initiatives like ChatGPT and DeepSeek3.1 [3]

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].

作者 | 玉盘 AI 团队 审核 | 华卫 "大模型每生成 1 美元价值，需支付 3 美元算力成本"，算力成本挑战已无争议。从软件层面的各类优化 方案层出不穷，真正从硬件源头着手的方案却屈指可数，市面上能看到的包括 Groq 在内的新计算硬件 也多数在大模型爆发前定型，难以充分匹配大模型本身的需求。 DeepSeek 从用户角度的不少构想与玉盘 SRDA 在做的事不谋而合，包括 IO 融合、3D 堆叠 DRAM 等， 而玉盘进一步提出了更完整的架构设计，或正式拉开下一代大模型专用计算架构的序幕。 今天，国内团队玉盘 AI 发布《SRDA AI 大模型专用计算架构》白皮书，提出了一种全新的计算架构： 系统级精简可重构数据流架构 SRDA （System-level Simplified Reconfigurable Dataflow Architecture）， 从硬件源头解决当前 AI 算力的核心瓶颈。 与此同时，DeepSeek 于半个月前发表论文《Insights into DeepSeek-V3: Scaling Challenges and Reflections on Hardware for AI ...