公众号记得加星标⭐️，第一时间看推送不会错过。 多年来，虚拟或云无线接入网 (RAN) 概念的主要问题之一在于英特尔作为通用芯片的唯一供应商。 这与相关的开放式 RAN 运动及其最初倡导的供应商多元化理念背道而驰。虽然其他公司也生产中央 处理器 (CPU)，但没有一家公司能像英特尔那样在 RAN 技术上投入如此巨资。像 Orange 这样的运 营商一再呼吁硬件和软件"完全解耦"，并实现"在任何类型的硬件上运行任何类型的软件"。然而，即 使是从英特尔转向使用相同 x86 架构的 CPU 竞争对手 AMD，也显得困难重重。最近 AI-RAN 的出 现更是雪上加霜。 根据英伟达的定义，AI-RAN 将用其图形处理器 (GPU) 取代传统 RAN 的定制芯片和虚拟 RAN 的 中央处理器 (CPU)。其部分原因是希望通过人工智能和机器学习来提高频谱效率——英伟达坚称，旧 硬件平台无法实现如此显著的频谱效率提升。然而，对于电信行业而言，不利之处在于，英伟达目前 在 GPU 领域的统治地位甚至超过了英特尔在 CPU 领域的统治地位。 人工智能替代方案即将问世吗？近来，谷歌内部研发的一款名为张量处理单元 (TPU) 的芯片 ...

Core Viewpoint - The article discusses the transformative shift from traditional software programming to AI software modeling, highlighting the implications for processing hardware and the development of dedicated AI accelerators. Group 1: Traditional Software Programming - Traditional software programming is based on writing explicit instructions to complete specific tasks, making it suitable for predictable and reliable scenarios [2] - As tasks become more complex, the size and complexity of codebases increase, requiring manual updates by programmers, which limits dynamic adaptability [2] Group 2: AI Software Modeling - AI software modeling represents a fundamental shift in problem-solving approaches, allowing systems to learn patterns from data through iterative training [3] - AI utilizes probabilistic reasoning to make predictions and decisions, enabling it to handle uncertainty and adapt to changes [3] - The complexity of AI systems lies in the architecture and scale of the models rather than the amount of code written, with advanced models containing hundreds of billions to trillions of parameters [3] Group 3: Impact on Processing Hardware - The primary architecture for executing software programs has been the CPU, which processes instructions sequentially, limiting its ability to handle the parallelism required for AI models [4] - Modern CPUs have adopted multi-core and multi-threaded architectures to improve performance, but still lack the massive parallelism needed for AI workloads [4][5] Group 4: AI Accelerators - GPUs have become the backbone of AI workloads due to their unparalleled parallel computing capabilities, offering performance levels in the range of petaflops [6] - However, GPUs face efficiency bottlenecks during inference, particularly with large language models (LLMs), where theoretical peak performance may not be achieved [6][7] - The energy demands of AI data centers pose sustainability challenges, prompting the industry to seek more efficient alternatives, such as dedicated AI accelerators [7] Group 5: Key Attributes of AI Accelerators - AI processors require unique attributes not found in traditional CPUs, with batch size and token throughput being critical for performance [8] - Larger batch sizes can improve throughput but may lead to increased latency, posing challenges for real-time applications [12] Group 6: Overcoming Hardware Challenges - The main bottleneck for AI accelerators is memory bandwidth, often referred to as the memory wall, which affects performance when processing large batches [19] - Innovations in memory architecture, such as high bandwidth memory (HBM), can help alleviate memory access delays and improve overall efficiency [21] - Dedicated hardware accelerators designed for LLM workloads can significantly enhance performance by optimizing data flow and minimizing unnecessary data movement [22] Group 7: Software Optimization - Software optimization plays a crucial role in leveraging hardware capabilities, with highly optimized kernels for LLM operations improving performance [23] - Techniques like gradient checkpointing and pipeline parallelism can reduce memory usage and enhance throughput [23][24]