Memristive devices for stochastic computing

Gaba, Siddharth; Knag, Phil; Zhang, Zhengya; Lü, Wei

doi:10.1109/iscas.2014.6865703

Cited by 54 publications

(38 citation statements)

References 15 publications

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“…In addition to probabilistic nature of these capture and emission in/from these defects, creation and rupture of these nano-filaments are also a probabilistic phenomenon causing considerable cycle-to-cycle (programming) conductance fluctuation [36]. Therefore, an extremely rich degree of stochasticity is available to ReRAM-based TRNGs to harvest [37][38][39].…”

Section: A Noisementioning

confidence: 99%

Nano-Intrinsic True Random Number Generation: A Device to Data Study

Kim

Nili

Truong

et al. 2019

IEEE Trans. Circuits Syst. I

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Recent advances in predictive data analytics and ever growing digitalization and connectivity with explosive expansions in industrial and consumer Internet-of-Things (IoT) has raised significant concerns about security of people's identities and data. It has created close to ideal environment for adversaries in terms of the amount of data that could be used for modeling and also greater accessibility for side-channel analysis of security primitives and random number generators. Random number generators (RNGs) are at the core of most security applications. Therefore, a secure and trustworthy source of randomness is required to be found. Here, we present a differential circuit for harvesting one of the most stochastic phenomenon in solidstate physics, random telegraphic noise (RTN), that is designed to demonstrate significantly lower sensitivities to other sources of noises, radiation and temperature fluctuations. We use RTN in amorphous SrTiO3-based resistive memories to evaluate the proposed true random number generator (TRNG). Successful evaluation on conventional true randomness tests (NIST tests) has been shown. Robustness against using predictive machine learning and side-channel attacks have also been demonstrated in comparison with non-differential readouts methods.

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Section: A Noisementioning

confidence: 99%

Nano-Intrinsic True Random Number Generation: A Device to Data Study

Kim

Nili

Truong

et al. 2019

IEEE Trans. Circuits Syst. I

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“…The search for a true RNG with scalable size, low power and insensitiveness to external parameters, such as temperature, is a key effort for the development of encryption systems. Several concepts for true RNG were previously proposed [1][2][3][4][5][6]. These include the thermal noise in electronic circuit [1] and the random telegraph noise (RTN) in nanodevices, such as dielectric layers [2] or resistive switching memory (RRAM) [3].…”

Section: Introductionmentioning

confidence: 99%

“…Unfortunately, the resistance window of STT MRAM is quite limited, making the sensing of the random bit quite challenging. Switching variability in RRAM was also used to generate random bits for stochastic computing, where any number (e.g., 0.4) can be represented by a sequence of random bits representing 0 and 1 (e.g., 60% of 0 and 40% of 1) [5,6]. This highlights the potential of RRAM devices for true RNG.…”

Section: Introductionmentioning

confidence: 99%

True Random Number Generation by Variability of Resistive Switching in Oxide-Based Devices

Balatti

Ambrogio

Wang

et al. 2015

IEEE J. Emerg. Sel. Topics Circuits Syst.

120

103

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Scalable, low-power random number generator (RNG) blocks are essential for encryption in today's communication systems. To allow for true RNG, a system must display an inherently-random physical phenomenon, such as the timing of individual fluctuations in random telegraph noise (RTN) or the random trapping/detrapping phenomena in dielectrics. In this work, a true RNG based on set variability in a resistive switching memory (RRAM) is demonstrated. The RNG relies on a single RRAM device, which is repeatedly programmed at a constant voltage close to the nominal set voltage. Due to the statistical variability of the set voltage, set transition takes place only in 50% of the applied pulses, thus resulting in a bimodal distribution of resistance. The bimodal distribution of analog resistance is finally converted into a 0/1 distribution of output voltage values through digital regeneration with a CMOS inverter. Index Terms-Nonvolatile memory, resistive-switching memory (RRAM), random number generation, memory reliability.

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“…The accuracy of the stochastic multiplication increases with the length of the stochastic bitstream, where www.advelectronicmat.de doubling the number of bits for each stochastic number can add a bit of extra precision to the multiplication. For instance, Gaba et al [131,171] demonstrated stochastic computing with random input spiking signals obtained by the stochastic switching of RRAM devices. [164] Another concern is that A and B should be statistically independent in Equation (3).…”

Section: Stochastic Computingmentioning

confidence: 99%

Stochastic Memory Devices for Security and Computing

Carboni

Ielmini

2019

Adv Elect Materials

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With the widespread use of mobile computing and internet of things, secured communication and chip authentication have become extremely important. Hardware‐based security concepts generally provide the best performance in terms of a good standard of security, low power consumption, and large‐area density. In these concepts, the stochastic properties of nanoscale devices, such as the physical and geometrical variations of the process, are harnessed for true random number generators (TRNGs) and physical unclonable functions (PUFs). Emerging memory devices, such as resistive‐switching memory (RRAM), phase‐change memory (PCM), and spin‐transfer torque magnetic memory (STT‐MRAM), rely on a unique combination of physical mechanisms for transport and switching, thus appear to be an ideal source of entropy for TRNGs and PUFs. An overview of stochastic phenomena in memory devices and their use for developing security and computing primitives is provided. First, a broad classification of methods to generate true random numbers via the stochastic properties of nanoscale devices is presented. Then, practical implementations of stochastic TRNGs, such as hardware security and stochastic computing, are shown. Finally, future challenges to stochastic memory development are discussed.

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Memristive devices for stochastic computing

Cited by 54 publications

References 15 publications

Nano-Intrinsic True Random Number Generation: A Device to Data Study

Nano-Intrinsic True Random Number Generation: A Device to Data Study

True Random Number Generation by Variability of Resistive Switching in Oxide-Based Devices

Stochastic Memory Devices for Security and Computing

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