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Flipping Bits in Memory Without Accessing Them: An Experimental Study of DRAM Disturbance Errors

机译:翻转内存中的位而不访问它们:DRAM干扰错误的实验研究

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Memory isolation is a key property of a reliable and secure computing system - an access to one memory address should not have unintended side effects on data stored in other addresses. However, as DRAM process technology scales down to smaller dimensions, it becomes more difficult to prevent DRAM cells from electrically interacting with each other. In this paper, we expose the vulnerability of commodity DRAM chips to disturbance errors. By reading from the same address in DRAM, we show that it is possible to corrupt data in nearby addresses. More specifically, activating the same row in DRAM corrupts data in nearby rows. We demonstrate this phenomenon on Intel and AMD systems using a malicious program that generates many DRAM accesses. We induce errors in most DRAM modules (110 out of 129) from three major DRAM manufacturers. From this we conclude that many deployed systems are likely to be at risk. We identify the root cause of disturbance errors as the repeated toggling of a DRAM row's wordline, which stresses inter-cell coupling effects that accelerate charge leakage from nearby rows. We provide an extensive characterization study of disturbance errors and their behavior using an FPGA-based testing platform. Among our key findings, we show that (ⅰ) it takes as few as 139K accesses to induce an error and (ⅱ) up to one in every 1.7K cells is susceptible to errors. After examining various potential ways of addressing the problem, we propose a low-overhead solution to prevent the errors.
机译:内存隔离是可靠和安全的计算系统的关键属性-访问一个内存地址不应对存储在其他地址中的数据产生意外的副作用。然而,随着DRAM处理技术按比例缩小到较小尺寸,防止DRAM单元彼此电相互作用变得更加困难。在本文中,我们将商品DRAM芯片的脆弱性暴露给干扰错误。通过读取DRAM中的相同地址,我们表明可能破坏附近地址中的数据。更具体地说,激活DRAM中的同一行会破坏附近行中的数据。我们使用产生许多DRAM访问的恶意程序在Intel和AMD系统上演示了这种现象。我们会导致来自三个主要DRAM制造商的大多数DRAM模块(129个中的110个)出错。由此得出的结论是,许多已部署的系统可能会受到威胁。我们将干扰错误的根本原因确定为DRAM行字线的反复翻转,这会引起单元间耦合效应,从而加速电荷从附近行泄漏。我们使用基于FPGA的测试平台对干扰错误及其行为进行了广泛的表征研究。在我们的主要发现中,我们显示(ⅰ)只需13.9万次访问即可引发错误,并且(ⅱ)每1.7K个单元中最多有1个易受错误影响。在研究了解决该问题的各种可能方法之后,我们提出了一种开销很小的解决方案来防止出现错误。

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