D-RaNGe: Using Commodity DRAM Devices to Generate True Random Numbers with Low Latency and High Throughput
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Abstract (arXiv)
We propose a new DRAM-based true random number generator (TRNG) that leverages DRAM cells as an entropy source. The key idea is to intentionally violate the DRAM access timing parameters and use the resulting errors as the source of randomness. Our technique specifically decreases the DRAM row activation latency (timing parameter tRCD) below manufacturer-recommended specifications, to induce read errors, or activation failures, that exhibit true random behavior. We then aggregate the resulting data from multiple cells to obtain a TRNG capable of providing a high throughput of random numbers at low latency. To demonstrate that our TRNG design is viable using commodity DRAM chips, we rigorously characterize the behavior of activation failures in 282 state-of-the-art LPDDR4 devices from three major DRAM manufacturers. We verify our observations using four additional DDR3 DRAM devices from the same manufacturers. Our results show that many cells in each device produce random data that remains robust over both time and temperature variation. We use our observations to develop D-RanGe, a methodology for extracting true random numbers from commodity DRAM devices with high throughput and low latency by deliberately violating the read access timing parameters. We evaluate the quality of our TRNG using the commonly-used NIST statistical test suite for randomness and find that D-RaNGe: 1) successfully passes each test, and 2) generates true random numbers with over two orders of magnitude higher throughput than the previous highest-throughput DRAM-based TRNG.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Aug 2018.
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Citing papers per year: 2022: 4; 2023: 1; 2024: 0; 2025: 1.
6 papers.
- 2022Machine Learning Training on a Real Processing-in-Memory Systemcited by 4
- 2022Accelerating Neural Network Inference with Processing-in-DRAM: From the Edge to the Cloudcited by 4
- 2022An Experimental Evaluation of Machine Learning Training on a Real Processing-in-Memory Systemcited by 2
- 2022Heterogeneous Data-Centric Architectures for Modern Data-Intensive Applications: Case Studies in Machine Learning and Databasescited by 1
- 2023TransPimLib: A Library for Efficient Transcendental Functions on Processing-in-Memory Systemscited by 1
- 2025EIM-TRNG: Obfuscating Deep Neural Network Weights with Encoding-in-Memory True Random Number Generator via RowHammercited by 0
- 2025EIM-TRNG: Obfuscating Deep Neural Network Weights with Encoding-in-Memory True Random Number Generator via RowHammer3 Jul 2025
- 2023TransPimLib: A Library for Efficient Transcendental Functions on Processing-in-Memory Systems3 Apr 2023
- 2022Accelerating Neural Network Inference with Processing-in-DRAM: From the Edge to the Cloud19 Sep 2022
- 2022An Experimental Evaluation of Machine Learning Training on a Real Processing-in-Memory System16 Jul 2022
- 2022Machine Learning Training on a Real Processing-in-Memory System13 Jun 2022
- 2022Heterogeneous Data-Centric Architectures for Modern Data-Intensive Applications: Case Studies in Machine Learning and Databases29 May 2022