SAFARI Live Seminar: High-Throughput TRNG Using Quadruple Row Activation in Commodity DRAM Chips

Title: QUAC-TRNG: High-Throughput True Random Number Generation Using Quadruple Row Activation in Commodity DRAM Chips
Speaker: Ataberk Olgun, TOBB University of Economics and Technology & ETH Zurich, https://www.ataberkolgun.com/

SAFARI Live Seminar Series Summer 2021 Talk #6
https://safari.ethz.ch/safari-live-se...
https://safari.ethz.ch/safari-seminar...

Abstract:
True random number generators (TRNG) sample random physical processes to create large amounts of random numbers for various use cases, including security-critical cryptographic primitives, scientific simulations, machine learning applications, and even recreational entertainment. Unfortunately, not every computing system is equipped with dedicated TRNG hardware, limiting the application space and security guarantees for such systems. To open the application space and enable security guarantees for the overwhelming majority of computing systems that do not necessarily have dedicated TRNG hardware, we develop QUAC-TRNG.

QUAC-TRNG exploits the new observation that a carefully-engineered sequence of DRAM commands activates four consecutive DRAM rows in rapid succession. This QUadruple ACtivation (QUAC) causes the bitline sense amplifiers to non-deterministically converge to random values when we activate four rows that store conflicting data because the net deviation in bitline voltage fails to meet reliable sensing margins.

We experimentally demonstrate that QUAC reliably generates random values across 136 commodity DDR4 DRAM chips from one major DRAM manufacturer. We describe how to develop an effective TRNG (QUAC-TRNG) based on QUAC. We evaluate the quality of our TRNG using NIST STS and find that QUAC-TRNG successfully passes each test. Our experimental evaluations show that QUAC-TRNG generates true random numbers with a throughput of 3.44 Gb/s (per DRAM channel), outperforming the state-of-the-art DRAM-based TRNG by 15.08x and 1.41x for basic and throughput-optimized versions, respectively. We show that QUAC-TRNG utilizes DRAM bandwidth better than the state-of-the-art, achieving up to 2.03x the throughput of a throughput-optimized baseline when scaling bus frequencies to 12 GT/s.

Bio:
Ataberk Olgun received his BSc degree in Computer Engineering from TOBB University of Economics and Technology, where he is currently studying for a Masters Degree. He joined SAFARI Research Group as an undergraduate intern in 2019. Since then he has worked on many projects on DRAM, Security, and Processing-in-Memory.

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Ataberk Olgun, Minesh Patel, A. Giray Yaglikci, Haocong Luo, Jeremie S. Kim, F. Nisa Bostanci, Nandita Vijaykumar, Oguz Ergin, and Onur Mutlu, “QUAC-TRNG: High-Throughput True Random Number Generation Using Quadruple Row Activation in Commodity DRAM Chips” Proceedings of the 48th International Symposium on Computer Architecture (ISCA), Virtual, June 2021.
Paper: http://people.inf.ethz.ch/omutlu/pub/...
Long talk video: QUAC-TRNG: High-Throughput True Rando...
Long talk slides (pptx): http://people.inf.ethz.ch/omutlu/pub/...
Long talk slides (pdf): http://people.inf.ethz.ch/omutlu/pub/...

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