论文标题
启用有效缓解错误芯片的记忆芯片的有效错误校正代码
Enabling Effective Error Mitigation in Memory Chips That Use On-Die Error-Correcting Codes
论文作者
论文摘要
储层计算是预测湍流的有力工具,其简单的架构具有处理大型系统的计算效率。然而,其实现通常需要完整的状态向量测量和系统非线性知识。我们使用非线性投影函数将系统测量扩展到高维空间,然后将其输入到储层中以获得预测。我们展示了这种储层计算网络在时空混沌系统上的应用,该系统模拟了湍流的若干特征。我们表明,使用径向基函数作为非线性投影器,即使只有部分观测并且不知道控制方程,也能稳健地捕捉复杂的系统非线性。最后,我们表明,当测量稀疏、不完整且带有噪声,甚至控制方程变得不准确时,我们的网络仍然可以产生相当准确的预测,从而为实际湍流系统的无模型预测铺平了道路。
Improvements in main memory storage density are primarily driven by process technology scaling, which negatively impacts reliability by exacerbating various circuit-level error mechanisms. To compensate for growing error rates, both memory manufacturers and consumers use error-mitigation mechanisms that improve manufacturing yield and allow system designers to meet reliability targets. Developing effective error mitigations requires understanding the errors' characteristics (e.g., worst-case behavior, statistical properties). Unfortunately, we observe that proprietary on-die Error-Correcting Codes (ECC) used in modern memory chips introduce new challenges to efficient error mitigation by obfuscating CPU-visible error characteristics in an unpredictable, ECC-dependent manner. This dissertation builds a detailed understanding of how on-die ECC obfuscates the statistical properties of main memory error mechanisms using a combination of real-chip experiments and statistical analyses. We experimentally study memory errors, examine how on-die ECC obfuscates their statistical characteristics, and develop new testing techniques to overcome the obfuscation. Our results show that the obfuscated error characteristics can be recovered using new memory testing techniques that exploit the interaction between on-die ECC and the statistical characteristics of memory error mechanisms to expose physical cell behavior. We conclude by discussing the critical need for transparency in DRAM reliability characteristics in order to enable DRAM consumers to better understand and adapt commodity DRAM chips to their system-specific needs. We hope and believe that the analysis, techniques, and results we present in this dissertation will enable the community to better understand and tackle current and future reliability challenges as well as adapt commodity memory to new advantageous applications.