Core Insights - The focus of academia and industry has shifted towards VLA (Vision-Language Action) in autonomous driving, which provides human-like reasoning capabilities for vehicle decision-making [1][4] - Traditional methods in perception and lane detection have matured, leading to decreased attention in these areas, while VLA is now a critical area for development among major autonomous driving companies [4][6] Summary by Sections Introduction to VLA - VLA is categorized into modular VLA, integrated VLA, and reasoning-enhanced VLA, which are essential for improving the reliability and safety of autonomous driving [1][4] Course Overview - A comprehensive course on autonomous driving VLA has been designed, covering foundational principles to practical applications, including cutting-edge algorithms like CoT, MoE, RAG, and reinforcement learning [6][12] Course Structure - The course consists of six chapters, starting with an introduction to VLA algorithms, followed by foundational algorithms, VLM as an interpreter, modular and integrated VLA, reasoning-enhanced VLA, and a final project [12][20] Chapter Highlights - Chapter 1 provides an overview of VLA algorithms and their development history, along with benchmarks and evaluation metrics [13] - Chapter 2 focuses on the foundational knowledge of Vision, Language, and Action modules, including the deployment of large models [14] - Chapter 3 discusses VLM's role as an interpreter in autonomous driving, covering classic and recent algorithms [15] - Chapter 4 delves into modular and integrated VLA, emphasizing the evolution of language models in planning and control [16] - Chapter 5 explores reasoning-enhanced VLA, introducing new modules for decision-making and action generation [17][19] Learning Outcomes - The course aims to deepen understanding of VLA's current advancements, core algorithms, and applications in projects, benefiting participants in internships and job placements [24]

Group 1 - The traditional von Neumann architecture is facing limitations due to storage and power walls, prompting interest in neuromorphic computing as a potential solution [1] - Neuromorphic chips, which mimic human brain computation principles, are seen as a disruptive force in the edge AI industry due to their significantly lower power consumption, potentially achieving energy savings of up to 1000 times compared to traditional solutions [1] - IBM's NorthPole chip has demonstrated a fivefold increase in energy efficiency compared to Nvidia's H100 GPU, indicating the potential of neuromorphic computing in reducing power consumption [1] Group 2 - Innatera has launched its first commercial brain-like microcontroller, Pulsar, which is designed for high-efficiency edge AI inference, achieving a 100-fold reduction in latency compared to traditional AI processors [2] - Pulsar claims to have a power consumption that is 500 times lower than traditional AI processors, utilizing low-power PLL and software-controlled voltage domains to optimize energy use [2][4] - The architecture of Pulsar integrates fully programmable spiking neural networks (SNN) optimized for asynchronous and sparse data computation, supporting heterogeneous computing [2] Group 3 - Polyn Technology has successfully fabricated its first neuromorphic analog signal processing chip, NASP, which is expected to enter the market in Q2 2025 [5] - NASP operates at ultra-low power levels, with consumption below 100μW during signal inference, and can drop to 30μW in specific applications, making it suitable for power-constrained environments [6] - The NASP platform can reduce raw data volume by up to 1000 times, enhancing privacy and reducing reliance on cloud services, particularly in sensitive fields like healthcare [6] Group 4 - The SENNA chip developed by Fraunhofer IIS is designed for processing spiking neural networks (SNN) and can handle low-dimensional time series data efficiently, with a response time of just 20 nanoseconds [12][14] - SENNA's architecture allows for direct processing of spiking input and output signals, making it suitable for real-time evaluation of event-based sensor data [14] - The chip is fully programmable, allowing developers to modify SNN models and reprogram the chip post-manufacturing, enhancing its flexibility for various applications [15] Group 5 - Neuromorphic computing is characterized by its structure, which includes neuron computation, synaptic weight storage, and routing communication, primarily utilizing spiking neural networks (SNN) [17] - The technology is divided into three categories based on implementation: digital CMOS, mixed-signal CMOS, and new device-based systems like memristors, with digital CMOS being the most commercially viable [19][20] - Various companies and institutions, including Tsinghua University and Zhejiang University, are actively researching neuromorphic computing chips, focusing on edge AI applications [21] Group 6 - The edge AI landscape is being transformed by neuromorphic computing, which offers significant energy efficiency and parallel processing capabilities compared to traditional architectures [23] - Existing neuromorphic chips like Intel's Loihi and IBM's TrueNorth have shown great potential in edge AI scenarios, with commercial applications already being explored by various manufacturers [23]