In this work, we experimentally demonstrate that it is possible to generate true random numbers at high throughput and low latency in commercial off-the-shelf (COTS) DRAM chips by leveraging simultaneous multiple-row activation (SiMRA) via an extensive characterization of 96 DDR4 DRAM chips. We rigorously analyze SiMRA's true random generation potential in terms of entropy, latency, and throughput for varying numbers of simultaneously activated DRAM rows (i.e., 2, 4, 8, 16, and 32), data patterns, temperature levels, and spatial variations. Among our 11 key experimental observations, we highlight four key results. First, we evaluate the quality of our TRNG designs using the commonly-used NIST statistical test suite for randomness and find that all SiMRA-based TRNG designs successfully pass each test. Second, 2-, 8-, 16-, and 32-row activation-based TRNG designs outperform the state-of-theart DRAM-based TRNG in throughput by up to 1.15x, 1.99x, 1.82x, and 1.39x, respectively. Third, SiMRA's entropy tends to increase with the number of simultaneously activated DRAM rows. Fourth, operational parameters and conditions (e.g., data pattern and temperature) significantly affect entropy. For example, for most of the tested modules, the average entropy of 32-row activation is 2.51x higher than that of 2-row activation. For example, increasing the temperature from 50{\deg}C to 90{\deg}C decreases SiMRA's entropy by 1.53x for 32-row activation. To aid future research and development, we open-source our infrastructure at https://github.com/CMU-SAFARI/SiMRA-TRNG.
翻译:在本工作中,我们通过对96个DDR4 DRAM芯片进行广泛表征,实验证明了利用同步多行激活技术,能够在商用现货DRAM芯片中以高吞吐量和低延迟生成真随机数。我们针对不同数量的同步激活DRAM行(即2、4、8、16和32行)、数据模式、温度水平和空间变化,从熵、延迟和吞吐量三个方面严格分析了SiMRA的真随机生成潜力。在我们获得的11项关键实验观察中,我们重点强调四项核心结果。首先,我们使用常用的NIST随机性统计测试套件评估了所设计TRNG的质量,发现所有基于SiMRA的TRNG设计均成功通过了各项测试。其次,基于2行、8行、16行和32行激活的TRNG设计在吞吐量上分别以最高1.15倍、1.99倍、1.82倍和1.39倍优于最先进的基于DRAM的TRNG。第三,SiMRA的熵值倾向于随着同步激活DRAM行数的增加而增加。第四,操作参数和条件(例如数据模式和温度)显著影响熵值。例如,对于大多数测试模块,32行激活的平均熵值比2行激活高2.51倍。又如,将温度从50°C升高到90°C会使32行激活的SiMRA熵值降低1.53倍。为促进未来的研究与开发,我们在https://github.com/CMU-SAFARI/SiMRA-TRNG开源了我们的基础设施。