Core Insights - The article emphasizes the shift in processor design focus from solely performance to also include power efficiency, as performance improvements that lead to disproportionate power increases may no longer be acceptable [1][2] - Current architectures are facing challenges in achieving further performance and power efficiency improvements, necessitating a reevaluation of microarchitecture designs [1][3] Group 1: Power Efficiency and Architecture - Processor designers are re-evaluating microarchitectures to control power consumption, with many efficiency improvements still possible through better design of existing architectures [1][2] - Advancements in process technology, such as moving to smaller nodes like 12nm, continue to be a primary method for reducing power consumption [1][2] - 3D-IC technology offers a new power efficiency point, providing lower power and higher speed compared to traditional PCB connections [2][3] Group 2: Implementation Challenges - Asynchronous design presents challenges, as it can lead to unpredictable performance and increased complexity, which may negate potential power savings [3][4] - Techniques like data and clock gating can help reduce power consumption, but they require careful analysis to identify major contributors to power usage [3][4] - The article notes that the most significant power savings opportunities lie at the architecture level rather than the RTL (Register Transfer Level) implementation [3][4] Group 3: AI and Performance Trade-offs - The rise of AI computing has pushed design teams to address the memory wall, balancing execution power and data movement power [5][6] - Architectural features such as speculative execution, out-of-order execution, and limited parallelism are highlighted as complex changes made to improve performance [5][6] - The article discusses the trade-offs between the complexity of features like branch prediction and their impact on area and power consumption [9][10] Group 4: Parallelism and Programming Challenges - Parallelism is identified as a key method for improving performance, but current processors have limited parallelism capabilities [10][11] - The article highlights the challenges of explicit parallel programming, which can deter software developers from utilizing multi-core processors effectively [13][14] - The potential for accelerators to offload tasks from CPUs is discussed, emphasizing the need for efficient design to improve overall system performance [15][16] Group 5: Custom Accelerators and Future Directions - Custom accelerators, particularly NPUs (Neural Processing Units), are gaining attention for their ability to optimize power and performance for specific AI workloads [17][18] - The article suggests that creating application-specific NPUs can significantly enhance efficiency, with reported improvements in TOPS/W and utilization [18][19] - The industry may face a risk of creative stagnation, necessitating new architectural concepts to overcome existing limitations [19]

来源：内容 编译自 IEEE ，谢谢。 如果将过多的铜线捆扎在一起，最终会耗尽空间——前提是它们不会先熔合在一起。人工智能数据 中心在GPU和内存之间传输数据的电子互连方面也面临着类似的限制。为了满足人工智能的海量 数据需求，业界正在转向更大尺寸、更多处理器的芯片，这意味着在机架内实现更密集、更长距离 的连接。初创公司正在展示 GPU 如何摆脱铜互连，用光纤链路取而代之。 光纤链路对数据中心来说并不陌生。它们使用可插拔收发器在机架之间传输数据，将电信号转换为 光信号。为了提高能源效率，"将光学元件集成到芯片封装中一直是一个梦想，"加州大学圣巴巴拉 分校电气工程教授克林特·肖( Clint Schow)表示。这就是共封装光学器件（CPO），科技巨头们正 在全力支持它。英伟达 (Nvidia) 最近宣布量产一款网络交换机，该交换机使用嵌入在与交换机同 一基板上的光子调制器。"这震惊了整个行业，"加州桑尼维尔初创公司Avicena的首席执行官巴迪 亚·佩泽什基 (Bardia Pezeshki) 表示。 哥伦比亚大学电气工程教授、Xscape Photonics联合创始人Keren Bergman解释说， Nvid ...