Industry Investment Rating - The report does not explicitly provide an investment rating for the industry [1] Core Viewpoints - The report emphasizes the importance of high-quality computing power in driving the development of artificial intelligence (AI) and the digital economy [1] - It highlights the transition from quantity to quality in computing power development, focusing on efficiency, intelligence, and sustainability [1][13] - The report identifies key challenges such as insufficient computing power supply, low intelligence levels, and energy consumption issues [8][9][10] Development Status and Challenges Development Status - Policy: Governments worldwide are accelerating the construction of computing power competitiveness through policy support and strategic planning The US plans to invest over 2800 billion in chip technology and China's "East Data West Computing" project attracting over 435 billion yuan in direct investment [8] - Scale: Global computing power is expanding, with intelligent computing power growing by 136% year-on-year, reaching 335 EFLOPS by the end of 2023 [12][13] - Development Level: China's computing power industry is shifting from scale expansion to quality improvement, focusing on application-driven development and green energy efficiency [13][14] Challenges - Challenge 1: Insufficient computing power supply and mismatched demand, with AI model training requiring massive computing resources [14] - Challenge 2: Low intelligence levels of computing power, making it difficult to meet diverse application scenarios [15] - Challenge 3: Energy consumption and carbon emissions are significant issues, with computing centers consuming 1500 billion kWh of electricity in 2023 [16] - Challenge 4: Rising demand for diverse computing power, but the level of universal accessibility and affordability remains low [17] - Challenge 5: Incomplete supply chains and underdeveloped ecosystems, with compatibility issues between different hardware and software platforms [18] - Challenge 6: Simple performance evaluation systems, lacking comprehensive assessment of actual computing power performance [19] Definition, Connotation, and Characteristics Definition - High-quality computing power is defined as advanced computing capability based on the latest AI theories, combined with algorithms and data, driving productivity and economic development [20][21] Connotation - Technological Innovation: High-quality computing power serves as the main engine for AI model training and application, reducing the threshold for AI adoption [23] - Optimization of Production Factors: It optimizes the allocation of data and resources, enhancing the efficiency of production, distribution, and consumption [24] - Industrial Transformation: It drives the integration of advanced technologies with traditional industries, fostering new business models and services [24] Characteristics - High Computational Efficiency: Focuses on both theoretical and practical performance, with an average computational efficiency of 118 GFLOPS/W in China [25][26] - High Intelligence Efficiency: Combines efficient AI processing with intelligent optimization capabilities [27] - High Carbon Efficiency: Aims to maximize computing output with minimal carbon emissions, emphasizing lifecycle carbon management [28] - Accessibility: Ensures computing power is widely available and affordable, supporting diverse application scenarios [29] - Sustainability: Emphasizes technical compatibility, complete supply chains, and open industrial ecosystems [30] - Evaluability: Requires a comprehensive evaluation system to reflect the actual performance of computing power [31] Development Path and Outlook Development Path - System Design: Focuses on improving computational efficiency through optimized system architecture and resource management [32] - Collaborative Drive: Enhances intelligence efficiency through the integration of computing power, algorithms, and data [33] - Lifecycle Management: Promotes carbon efficiency through green procurement, design, and operation [34] - Infrastructure First: Advances universal accessibility and affordability by building intelligent computing centers [36] - Ecosystem Prosperity: Encourages sustainable development through open and standardized industrial ecosystems [38][39] - Diverse Evaluation: Accelerates the standardization of computing power development through comprehensive evaluation systems [42][44] Outlook - Market Environment: China's vast application market and supportive policies provide a strong foundation for the computing power industry [46] - Universal Accessibility: The trend towards universal accessibility will unlock the potential of AI across various fields [47] - Intelligent Upgrade: The integration of AI and computing power will drive deep economic and social development [48] Evaluation System Exploration Background - The evaluation system for computing power is transitioning from hardware-focused assessments to comprehensive evaluations that consider application performance and lifecycle carbon management [51][52] Principles - The evaluation system should be policy-aligned, systematic, targeted, comprehensive, operable, and adaptable to future changes [58] Practice - The "Five-in-One" evaluation system assesses computing power quality across five dimensions: computational efficiency, intelligence efficiency, carbon efficiency, accessibility, and sustainability [60][70] Significance - The evaluation system standardizes and accelerates the high-quality development of the computing power industry, providing guidance for technological innovation and infrastructure construction [70] Application Recommendations - Accelerate the development of supporting standards and tools to ensure the effective implementation of the evaluation system [71] - Conduct evaluation tests in typical AI application scenarios and expand theoretical research to support the industry's high-quality development [73]
人工智能算力高质量发展评估体系报告
中国信通院·2024-10-07 08:02