英特尔副总裁宋继强：AI计算重心正在向推理转移

Core Insights - The development of AI capabilities is transitioning from foundational large models to intelligent agents, focusing more on providing specific functions to build workflows [3][7] - Embodied intelligence, as a significant form of physical AI, integrates digital intelligence into physical devices for interaction with the real world, primarily emphasizing reasoning applications [3][7] AI Demand and Infrastructure - Industry analysts predict that the demand for AI computing power is shifting from training to inference, which will consume a corresponding proportion of computing resources [3][7] - The construction of multi-agent systems is essential for creating complete workflows and achieving parallel operations, necessitating heterogeneous infrastructure [3][7] Heterogeneous System Requirements - Heterogeneous systems must possess flexible support capabilities at three levels: an open AI software stack at the top layer, infrastructure that meets the needs of small and medium enterprises in the middle layer, and a bottom layer that integrates diverse hardware [3][7] - The bottom layer should include various architectures such as CPUs, GPUs, NPUs, AI accelerators, and brain-like computing devices to build a flexible heterogeneous system through layered infrastructure [3][7] Embodied Intelligence Robotics - In the field of embodied intelligent robotics, various methods for achieving intelligent tasks are being explored, from traditional layered custom models to end-to-end VLA models, with no optimal solution currently established [4][8] - Traditional industrial automation solutions focus on reliability, real-time performance, and computational accuracy, while large language model-based solutions lean towards neural network approaches requiring differentiated computing architectures [4][8] Future Challenges and Opportunities - The era of embodied intelligent robots is anticipated to bring challenges in computing power and energy consumption, with heterogeneous computing becoming the core architecture of AI infrastructure [4][8] - As the scale of robots reaches millions, they are expected to break through industrial scene limitations and widely support commercial and personalized applications, necessitating multi-agent systems [4][8][9]