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

机译:在没有访问的情况下翻转内存中的位:DRAM扰动误差的实验研究

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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 - 对一个存储器地址的访问不应对存储在其他地址中的数据的意外副作用。然而,由于DRAM工艺技术缩小到较小尺寸,因此更难以防止DRAM电池彼此电相互作用。在本文中,我们将商品DRAM芯片的脆弱性暴露给干扰错误。通过在DRAM中的同一地址读取,我们表明可以在附近地址损坏数据。更具体地说,在DRAM中激活相同的行损坏附近行中的数据。我们使用生成许多DRAM访问的恶意程序展示英特尔和AMD系统上的这种现象。我们从三大DRAM制造商中诱导大多数DRAM模块(110分中的110)。从这我们得出结论,许多部署的系统可能存在风险。我们识别干扰错误作为DRAM行字线的重复切换的根本原因,这强调了从附近行加速电荷泄漏的细胞间耦合效果。我们使用FPGA的测试平台提供了对干扰误差及其行为的广泛表征研究。在我们的关键发现中,我们表明(i)只需139k访问即可诱导错误,并且每次1.7k细胞中最多一个易受错误的误差。在检查解决问题的各种潜在方法后,我们提出了一个低开销解决方案来防止错误。

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