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Core Insights - The global tech industry is experiencing a shift from cloud-based AI to edge AI, driven by the limitations of cloud dependency and the need for real-time processing in critical applications [1][4][18] - The current trend emphasizes the development of smaller, more efficient AI models that can operate independently on edge devices, rather than relying on large cloud models [16][18] Group 1: Challenges of Cloud Dependency - Cloud-based AI systems face significant latency issues, which can be detrimental in time-sensitive applications like autonomous driving [2][4] - Privacy concerns arise from the need to transmit sensitive data to cloud servers, making edge computing a more attractive option for users [2][4] Group 2: The Shift to Edge AI - The industry is moving towards a "cloud-edge-end" architecture, where complex tasks are handled by cloud models while real-time tasks are managed by edge devices [7][18] - Edge AI must overcome the "impossible triangle" of high intelligence, low latency, and low power consumption, necessitating innovative solutions [7][8] Group 3: Techniques for Edge AI Implementation - Knowledge distillation is a key technique that allows smaller models to retain the intelligence of larger models by learning essential features and reasoning paths [8][10] - Extreme quantization reduces model size and increases speed by compressing model weights, allowing for efficient processing on edge devices [10][11] - Structural pruning eliminates redundant connections in neural networks, further optimizing performance for edge applications [10][11] Group 4: Hardware Innovations - The "memory wall" issue in traditional architectures leads to inefficiencies, prompting the development of specialized architectures that integrate storage and computation [11][13] - Companies are exploring dedicated chip designs that optimize performance for specific AI tasks, enhancing efficiency in edge computing [13][14] Group 5: Industry Evolution - The focus is shifting from general-purpose AI models to specialized models that excel in specific applications, improving reliability and performance [15][16] - The Chinese AI industry is collectively recognizing the importance of practical applications over sheer model size, leading to a more grounded approach to AI development [16][18]