标题： 打破HBM位成本障碍：面向AI推理基础设施的领域专用ECC 显示英文标题

标题： Breaking the HBM Bit Cost Barrier: Domain-Specific ECC for AI Inference Infrastructure

摘要： 高性能内存（HBM）为人工智能工作负载提供了卓越的带宽和能效，但其每个比特的高成本，部分是由于严格的芯片内可靠性要求，正成为可扩展部署的日益增长的障碍。 本研究通过消除芯片内ECC并将其所有故障管理转移到内存控制器，探索了一种系统级的成本降低方法。 我们引入了一个领域特定的ECC框架，结合大码字Reed--Solomon~(RS)纠正与轻量级细粒度CRC检测，差分奇偶校验更新以减轻写入放大效应，并根据数据重要性进行可调保护。 我们使用大型语言模型推理工作负载进行评估，结果显示，即使在原始HBM比特错误率高达$10^{-3}$的情况下，与配备理想无错误HBM的系统相比，该系统仍保留了超过78%的吞吐量和97%的模型准确率。 通过将可靠性视为可调的系统参数而非固定的硬件约束，我们的设计为人工智能基础设施中低成本、高性能HBM的部署开辟了一条新路径。 显示英文摘要

摘要： High-Bandwidth Memory (HBM) delivers exceptional bandwidth and energy efficiency for AI workloads, but its high cost per bit, driven in part by stringent on-die reliability requirements, poses a growing barrier to scalable deployment. This work explores a system-level approach to cost reduction by eliminating on-die ECC and shifting all fault management to the memory controller. We introduce a domain-specific ECC framework combining large-codeword Reed--Solomon~(RS) correction with lightweight fine-grained CRC detection, differential parity updates to mitigate write amplification, and tunable protection based on data importance. Our evaluation using LLM inference workloads shows that, even under raw HBM bit error rates up to $10^{-3}$, the system retains over 78\% of throughput and 97\% of model accuracy compared with systems equipped with ideal error-free HBM. By treating reliability as a tunable system parameter rather than a fixed hardware constraint, our design opens a new path toward low-cost, high-performance HBM deployment in AI infrastructure.