未来芯片工程师，应该具备的技能

Core Insights - The article discusses the integration of silicon chip design and system engineering, driven by advancements in artificial intelligence and the demand for more efficient computing infrastructure [2][3] - It highlights the shift in performance metrics for AI systems from traditional speed indicators to efficiency-based metrics, emphasizing the importance of cost and energy consumption [3][4] - The future economic growth is closely linked to AI productivity improvements, with the semiconductor industry playing a crucial role in this expansion [4][5] Group 1 - The concept of "convergence" symbolizes the merging of traditionally independent fields: silicon engineering and system engineering, necessitated by modern technologies like AI [3] - AI system performance is now measured by efficiency metrics such as "tokens per dollar" and "tokens per watt," reflecting the high costs and energy demands of large AI systems [3][4] - The global economy, currently valued at approximately $117 trillion, is expected to double in the next 25 years, largely driven by AI advancements [4] Group 2 - Four key components determining AI system performance are identified: computation, interconnect, storage, and power, with each presenting unique challenges [5] - The semiconductor supply chain is undergoing significant transformations as companies adapt their manufacturing processes and infrastructure to support the AI economy [5][6] - Future engineers will need interdisciplinary knowledge, with system engineers understanding semiconductor technology and chip designers grasping physical principles and system behavior [6]