Probabilistic Switching Research Articles

The influence of background turbulence on Ahmed-body wake bistability

The discovery of wake bistability has generated an upsurge in experimental investigations into the wakes of simplified vehicle geometries. Particular focus has centred on the probabilistic switching between two asymmetrical bistable wake states of a square-back Ahmed body; however, the majority of this research has been undertaken in wind tunnels with turbulence intensities of less than$1\,\%$, considerably lower than typical atmospheric levels. To better simulate bistability under on-road conditions, in which turbulence intensities can easily reach levels of$10\,\%$or more, this experimental study investigates the effects of free-stream turbulence on the bistability characteristics of the square-back Ahmed body. Through passive generation and quantification of the free-stream turbulent conditions, a monotonic correlation was found between the switching rate and free-stream turbulence intensity.

A multiscale magneto-thermo-mechanically coupled model for ultra-low-field induced magneto-elastocaloric effect in magnetostrictive-shape memory alloy composite system

Recent experimental results show that apparent magneto-elastocaloric effect can be induced in the magnetostrictive-shape memory alloy composite system under an ultra-low magnetic field. In this paper, a multiscale theoretical model is constructed to predict the magneto-thermo-mechanically coupled response of such a composite system by considering the multi-field interactions among the heterogeneous constituent elements in the grain, polycrystalline aggregate and macroscopic scales. In the grain scale, for the magnetostrictive alloy (MEA), the fluctuations of stress and magnetic fields caused by the interactions among the domains are addressed by the self-consistent homogenization scheme. Adopting a probabilistic domain switching criterion based energetic analysis, a constitutive model of MEA is established. For the shape memory alloy (SMA), a crystal plasticity based thermo-mechanically coupled constitutive model is constructed in the framework of irreversible thermodynamics. The interactions among austenite phase and martensite variants are considered by the Mori-Tanaka's homogenization scheme. Thermodynamic driving force for martensite transformation and the internal heat production originated from transformation latent heat and inelastic deformation dissipation are derived from the dissipative inequality and the conservation of energy, respectively. In the polycrystalline aggregate scale, to estimate the interactions among the grains and predict the overall responses of the polycrystalline aggregates of MEA and SMA, a unified incremental magneto-thermo-mechanically coupled self-consistent homogenization scheme is developed. In the macroscopic scale, the magneto-thermo-mechanical interaction equations among the MEA rod, SMA cuboid and Al alloy frame in the composite system are derived by considering the conditions of deformation compatibility, force balance and thermodynamic equilibrium. The capability of the proposed multiscale model to describe the magneto-elastocaloric effect of MEA-SMA composite system is validated by comparing the predictions with the existing experimental data. Moreover, the influences of geometric dimension, pre-load, frame's stiffness and crystallographic orientation on the magneto-elastocaloric effect of the composite system are discussed. The proposed model provides a theoretical guidance for the optimization design of solid-state refrigeration devices in both the microscopic and macroscopic scales.

Proposal for energy efficient spin transfer torque-magnetoresistive random access memory device

Utilizing the electronic analogs of optical phenomena such as anti-reflection coating and resonance for spintronic devices, we propose and theoretically analyze the design of a spin transfer torque-magnetoresistive random access memory (STT-MRAM) device. The proposed device consists of a superlattice heterostructure terminated with the anti-reflective regions sandwiched between the fixed and free ferromagnetic layers. Employing Green’s function spin-transport formalism coupled self-consistently with the stochastic Landau–Lifshitz–Gilbert–Slonczewski equation, we design an STT-MRAM based on the anti-reflective superlattice magnetic tunnel junction (AR-SLMTJ) device having an ultrahigh tunnel magnetoresistance (≈3.5×104%) and large spin current. We demonstrate that the STT-MRAM based on the AR-SLMTJ structure owing to the physics of bandpass spin filtering is nearly 1100% more energy efficient than trilayer magnetic tunnel junction (MTJ) based STT-MRAM. We also present detailed probabilistic switching and energy analysis to find out the optimal point of operation of a trilayer MTJ and AR-SLMTJ based STT-MRAM. Our predictions serve as a template to consider the heterostructures for next-generation spintronic device applications.

Probabilistic switching can solve the problem of costly prosocial exclusion

Public Goods Game (PGG) originates from “the Tragedy of Commons”, which is argued by the American scholar Hardin. PGG is a type of evolution game that these individuals in this game can affect each other, and reached a stable state. This paper demonstrates a new version PGG model which is an exclusion PGG model. Besides, combing Parrondo’s Paradox with this PGG model, and it also shows a similar “winning” outcome. Parrondo’s Paradox shows that two individual-losing games, when alternate, yield a winning outcome. Moreover, the additional periodic switching mechanism also applies to the PGG model and obtains a similar result when compared with that of conventional Parrondo’s Paradox. The biggest contribution of this paper is that Parrondo’s Paradox can fully applicable to the exclusion PGG model, and that simulation result also presents an effective result.

Theory and experimental verification of configurable computing with stochastic memristors

The inevitable variability within electronic devices causes strict constraints on operation, reliability and scalability of the circuit design. However, when a compromise arises among the different performance metrics, area, time and energy, variability then loosens the tight requirements and allows for further savings in an alternative design scope. To that end, unconventional computing approaches are revived in the form of approximate computing, particularly tuned for resource-constrained mobile computing. In this paper, a proof-of-concept of the approximate computing paradigm using memristors is demonstrated. Stochastic memristors are used as the main building block of probabilistic logic gates. As will be shown in this paper, the stochasticity of memristors’ switching characteristics is tightly bound to the supply voltage and hence to power consumption. By scaling of the supply voltage to appropriate levels stochasticity gets increased. In order to guide the design process of approximate circuits based on memristors a realistic device model needs to be elaborated with explicit emphasis of the probabilistic switching behavior. Theoretical formulation, probabilistic analysis, and simulation of the underlying logic circuits and operations are introduced. Moreover, the expected output behavior is verified with the experimental measurements of valence change memory cells. Hence, it is shown how the precision of the output is varied for the sake of the attainable gains at different levels of available design metrics. This approach represents the first proposition along with physical verification and mapping to real devices that combines stochastic memristors into unconventional computing approaches.

Probabilistic Resistive Switching Device Modeling Based on Markov Jump Processes

In this work, a versatile mathematical framework for multi-state probabilistic modeling of Resistive Switching (RS) devices is proposed for the first time. The mathematical formulation of memristor and Markov jump processes are combined and, by using the notion of master equations for finite-states, the inherent probabilistic time-evolution of RS devices is sufficiently modeled. In particular, the methodology is generic enough and can be applied for $N$ states; as a proof of concept, the proposed framework is further stressed for both two-state RS paradigm, namely $N=2$, and multi-state devices, namely $N=4$. The presented I-V results demonstrate in a qualitative and quantitative manner, adequate matching with other modeling approaches.

A Seamless, Reconfigurable, and Highly Parallel In-Memory Stochastic Computing Approach With Resistive Random Access Memory Array

A seamless, reconfigurable, and highly parallel in-memory stochastic computing (SC) approach is proposed and experimentally demonstrated in resistive random access memory (RRAM) array. By utilizing the probabilistic switching behavior of RRAM and parallel writing, stochastic bit streams (SBS) are generated in parallel. By employing in-memory stateful logic, parallel stochastic logic is directly performed within the same memory array. This in-memory SC approach has three merits: 1) true stochastic number generator (SNG) can be obtained with low cost, 2) the signal conversion overhead between SNG and stochastic logic is eliminated, 3) it is highly parallel and the stochastic logic is reconfigurable. This work provides an efficient and low-cost alternative approach to achieve SC.

Design, Analysis and Application of Embedded Resistive RAM Based Strong Arbiter PUF

Resistive Random Access Memory (RRAM) based Physical Unclonable Function (PUF) designs exploit either the probabilistic switching or the resistance variability during forming, SET and RESET processes of RRAM. Memory PUFs using RRAM are typically weak PUFs due to fewer number of challenge response pairs. We propose a strong arbiter PUF based on 1T-1R bit cell which is designed from conventional RRAM memory array with minimally invasive changes. Conventional voltage sense amplifier is repurposed to act like an arbiter and generate the response. Similarly, address and data lines are repurposed to act as challenge and response bits respectively. The PUF is simulated using 65 nm predictive technology models for CMOS and Verilog-A model for a hafnium oxide based RRAM. The proposed PUF architecture is evaluated for uniqueness, uniformity and reliability for various number of stages. It demonstrates mean intra-die Hamming Distance (HD) of 0.135 percent and inter-die HD of 51.4 percent, and passes the NIST tests. We study the vulnerability of proposed PUF to machine learning attacks. We also present an application of proposed PUF for data attestation in the internet of things. Proposed PUF-based data attestation consumes 9.88pJ of total energy per data block of 64-bits and offers a speed of 120.7 kbps.

Implementation of an efficient magnetic tunnel junction-based stochastic neural network with application to iris data classification

Stochastic neuromorphic computation (SNC) has the potential to enable a low power, error tolerant and scalable computing platform in comparison to its deterministic counterparts. However, the hardware implementation of complementary metal oxide semiconductor (CMOS)-based stochastic circuits involves conversion blocks that cost more than the actual processing circuits. The realization of the activation function for SNCs also requires a complicated circuit that results in a significant amount of power dissipation and area overhead. The inherent probabilistic switching behavior of nanomagnets provides an advantage to overcome these complexity issues for the realization of low power and area efficient SNC systems. This paper presents magnetic tunnel junction (MTJ)-based stochastic computing methodology for the implementation of a neural network. The stochastic switching behavior of the MTJ has been exploited to design a binary to stochastic converter to mitigate the complexity of the CMOS-based design. The paper also presents the technique for realizing stochastic sigmoid activation function using an MTJ. Such circuits are simpler than existing ones and use considerably less power. An image classification system employing the proposed circuits has been implemented to verify the effectiveness of the technique. The MTJ-based SNC system shows area and energy reduction by a factor of 13.5 and 2.5, respectively, while the prediction accuracy is 86.66%. Furthermore, this paper investigates how crucial parameters, such as stochastic bitstream length, number of hidden layers and number of nodes in a hidden layer, need to be set precisely to realize an efficient MTJ-based stochastic neural network (SNN). The proposed methodology can prove a promising alternative for highly efficient digital stochastic computing applications.

Interacting T-S fuzzy semantic model estimation for maneuvering target tracking

This paper proposes an interacting Takagi–Sugeno(T-S) fuzzy semantic model estimator (ITS-FSM) for maneuvering target tracking, and constructs a framework for a generic interacting T-S fuzzy semantic model to incorporate semantic information concerning the target. To adaptively calculate the transition probability matrix of the models, a probabilistic switching model based on semantic fuzzy sets is derived by using the degree of intersection between fuzzy sets, which enables switching from one semantic fuzzy set to another. We also propose an efficient kernel maximum entropy fuzzy clustering method to identify the premise parameters of the model. This enables the proposed algorithm to recursively estimate the premise parameters in case of a limited number of samples. Moreover, a modified extended forgetting factor recursive least squares (MEFRLS) estimator is used to identify the parameters of the T-S fuzzy semantic model. The results of experiments on three simulation datasets show that the proposed ITS-FSM algorithm is efficient, and is excellent at handling non-Gaussian noise.

Voltage-Controlled Binary Conductance Switching in Gold-4,4'-Bipyridine-Gold Single-Molecule Nanowires.

We investigate gold–4,4′-bipyridine–gold single-molecule junctions with the mechanically controllable break junction technique at cryogenic temperature (T = 4.2 K). We observe bistable probabilistic conductance switching between the two molecular binding configurations, influenced both by the mechanical actuation and by the applied voltage. We demonstrate that the relative dominance of the two conductance states is tunable by the electrode displacement, whereas the voltage manipulation induces an exponential speedup of both switching times. The detailed investigation of the voltage-tunable switching rates provides an insight into the possible switching mechanisms.

Analogue pattern recognition with stochastic switching binary CMOS-integrated memristive devices

Biological neural networks outperform current computer technology in terms of power consumption and computing speed while performing associative tasks, such as pattern recognition. The analogue and massive parallel in-memory computing in biology differs strongly from conventional transistor electronics that rely on the von Neumann architecture. Therefore, novel bio-inspired computing architectures have been attracting a lot of attention in the field of neuromorphic computing. Here, memristive devices, which serve as non-volatile resistive memory, are employed to emulate the plastic behaviour of biological synapses. In particular, CMOS integrated resistive random access memory (RRAM) devices are promising candidates to extend conventional CMOS technology to neuromorphic systems. However, dealing with the inherent stochasticity of resistive switching can be challenging for network performance. In this work, the probabilistic switching is exploited to emulate stochastic plasticity with fully CMOS integrated binary RRAM devices. Two different RRAM technologies with different device variabilities are investigated in detail, and their potential applications in stochastic artificial neural networks (StochANNs) capable of solving MNIST pattern recognition tasks is examined. A mixed-signal implementation with hardware synapses and software neurons combined with numerical simulations shows that the proposed concept of stochastic computing is able to process analogue data with binary memory cells.

Stochastic Computing Based on Volatile GeSe Ovonic Threshold Switching Selectors

Stochastic computing (SC) is a special type of digital compute strategy where values are represented by the probability of 1 and 0 in stochastic bit streams, which leads to superior hardware simplicity and error-tolerance. In this letter, we propose and demonstrate SC with GeSe-based Ovonic Threshold Switching (OTS) selector devices by exploiting their probabilistic switching behavior. The stochastic bit streams generated by OTS are demonstrated with good computation accuracy in both multiplication operation and image processing circuit. Moreover, the bit distribution has been statistically studied and linked to the collective defect de/localization behavior in the chalcogenide material. Weibull distribution of the delay time supports the origin of such probabilistic switching, facilitates further optimization of the operation condition, and lays the foundation for device modelling and circuit design. Considering its other advantages such as simple structure, fast speed, and volatile nature, OTS is a promising material for implementing SC in a wide range of novel applications, such as image processors, neural networks, control systems and reliability analysis.

From Charge to Spin and Spin to Charge: Stochastic Magnets for Probabilistic Switching

As the rapid pace of Moore's Law has been slowing down, there has been intense activity to “reinvent the transistor.” An emerging paradigm is to complement the existing complementary metal-oxide-semiconductor (CMOS) technology with new functionalities, rather than finding a drop-in replacement for it. In this article, we discuss such a complementary approach that we call probabilistic spin logic (PSL) based on the concept of a probabilistic or p-bit. p-bits fluctuate between 0 and 1 and can be imagined in between deterministic bits that are either 0 or 1 and quantum bits that are a superposition of 0 and 1. Interconnected circuits built out of p-bits (p-circuits) can be broadly useful for machine learning and quantum computing in the solution of problems that conventional CMOS may not be particularly suited for. Although such p-bits can be implemented using standard CMOS technology, we will show that the inherent physics of nanomagnets can naturally provide an energy efficient and scalable p-bit implementation through the use of low-barrier magnetic tunnel junctions (MTJs). In this article, we provide a general description of p-bits and p-circuits and discuss their applications. We review experimental progress toward constructing p-bits and p-circuits exploiting the inherent stochasticity of nanomagnets, from a physics/device/circuits perspective. In particular, we identify building blocks for “write” and “read” operations that can be used in different combinations to construct functional p-bits and p-circuits. Finally, we discuss the prospects and challenges of PSL as an emerging, unconventional computing paradigm for a beyond CMOS era.

High-Throughput/Low-Energy MTJ-Based True Random Number Generator Using a Multi-Voltage/Current Converter

This article introduces high-throughput/low-energy true random number generators (TRNGs) based on CMOS and three-terminal magnetic tunnel junction (MTJ) devices. MTJs are fast and probabilistic switching devices, which can be used as random number sources for TRNGs. However, as the switching probability is quite sensitive to the write current given to MTJs, precise closed-loop control is necessary. Thus, a high-complexity current control circuit is required, such as high precision digital-to-analog converters (DACs), occupying large area and causing large energy dissipation. In order to address the issue, we propose a multi-voltage/current ( $V/I$ ) converter capable of multilevel coarse current switching and fine adjusting within each level. The fine adjusting can be done by DACs with fewer bits, resulting in much smaller size and energy dissipation than a conventional single- $V/I$ converter. In addition, a multiple-writing scheme for three-terminal MTJs is proposed for increasing the throughput while maintaining the write power. The proposed TRNGs are designed using TSMC 65-nm CMOS and a three-terminal MTJ model that achieves a throughput of 333 Mb/s, an energy dissipation of 0.66 pJ/bit and an area of $2040~\mu {\rm m^{2}}$ . This result exhibits a $5\times $ throughput, a 93% energy reduction and an 86% area reduction in comparison with a conventional CMOS/MTJ-based TRNG.

Distributed Dynamic Self-Triggered Control for Uncertain Complex Networks With Markov Switching Topologies and Random Time-Varying Delay

This paper studies the synchronization of complex networks with probabilistic interval delay and Markov switching topologies by using a novel dynamic self-triggered control (DSTC) scheme. Employing the probability distribution information of the input time-delay, the control protocol is transformed into a new rule with stochastic parameters. Also, a more general case of switching topologies, Markov switching topologies with partial information on transition rate, is discussed. By introducing a free-connection weighting matrix scheme and using the stability theory, a less conservative synchronization result is derived. It is shown by a numerical example that the DSTC method can reduce the sampling frequency apparently, and the tolerable delay upper bound can also be relaxed. In addition, continuous communication and the Zeno-behavior can be avoided.

Learning Self-Awareness for Autonomous Vehicles: Exploring Multisensory Incremental Models

The technology for autonomous vehicles is close to replacing human drivers by artificial systems endowed with high-level decision-making capabilities. In this regard, systems must learn about the usual vehicle's behavior to predict imminent difficulties before they happen. An autonomous agent should be capable of continuously interacting with multi-modal dynamic environments while learning unseen novel concepts. Such environments are not often available to train the agent on it, so the agent should have an understanding of its own capacities and limitations. This understanding is usually called self-awareness. This paper proposes a multi-modal self-awareness modeling of signals coming from different sources. This paper shows how different machine learning techniques can be used under a generic framework to learn single modality models by using Dynamic Bayesian Networks. In the presented case, a probabilistic switching model and a bank of generative adversarial networks are employed to model a vehicle's positional and visual information respectively. Our results include experiments performed on a real vehicle, highlighting the potentiality of the proposed approach at detecting abnormalities in real scenarios.

Energy-efficient Design of MTJ-based Neural Networks with Stochastic Computing

Hardware implementations of Artificial Neural Networks (ANNs) using conventional binary arithmetic units are computationally expensive, energy-intensive, and have large area overheads. Stochastic Computing (SC) is an emerging paradigm that replaces these conventional units with simple logic circuits and is particularly suitable for fault-tolerant applications. We propose an energy-efficient use of Magnetic Tunnel Junctions (MTJs), a spintronic device that exhibits probabilistic switching behavior, as Stochastic Number Generators (SNGs), which forms the basis of our NN implementation in the SC domain. Further, the error resilience of target applications of NNs allows approximating the synaptic weights in our MTJ-based NN implementation, in ways brought about by properties of the MTJ-SNG, to achieve energy-efficiency. An algorithm is designed that, given an error tolerance, can perform such approximations in a single-layer NN in an optimal way owing to the convexity of the problem formulation. We then use this algorithm and develop a heuristic approach for approximating multi-layer NNs. Classification problems were evaluated on the optimized NNs and results showed substantial savings in energy for little loss in accuracy.

A new micromechanics based full field numerical framework to simulate the effects of dynamic recrystallization on the formability of HCP metals

This paper presents a new full-field, efficient and mesh-free numerical framework to model microstructure evolution, dynamic recrystallization (DRX) and formability in hexagonal closed-packed (HCP) metals such as magnesium alloys at warm temperatures. A rate tangent-fast Fourier transform-based elasto-viscoplastic crystal plasticity constitutive model for HCP metals (RTCP-FFT-HCP) is coupled with a probabilistic cellular automata (CA) approach to model DRX (CA-DRX). Furthermore, this new model is coupled with the Marciniak-Kuczynski (M-K) approach to model formability of magnesium alloys at elevated temperatures. The RTCP-FFT-HCP model computes macro stress-strain, twinning volume fraction, micromechanical fields, texture evolution and local dislocation density. Nucleation of new grains and their subsequent growth is modeled using the cellular automata approach with probabilistic state switching rule. First, the proposed RTCP-FFT-HCP model is validated by comparing the predicted stress-strain responses and texture evolution under uniaxial tension and compression with experimental measurements for AZ31 sheet alloy at room temperature. The coupled CA-RTCPFFT-HCP model is further validated by comparing the predicted stress-strain responses and texture evolution in uniaxial compression with experimental measurements at 100 °C, 200 °C and 300 °C for the AZ31 sheet alloy. Next, the forming limit diagrams (FLDs) with and without including the effects of DRX are simulated at 100 °C, 200 °C and 300 °C for AZ31 sheet alloy. The predicted FLDs with DRX show very good agreement with the experimental measurements and clearly demonstrate the need for an accurate DRX model. The study reveals that the DRX strongly affects the deformed grain structure, grain size and texture evolution and also highlights the importance accounting for DRX during FLD simulations at high temperatures.

A Moving Target Defense Control Framework for Cyber-Physical Systems

This paper considers the problem of efficiently and securely controlling cyber-physical systems that are operating in uncertain, and adversarial environments. To mitigate sensor, actuator attacks, and performance loss due to such attacks, we formulate a secure control algorithm that consists of a proactive and a reactive defense mechanism. The proactive mechanism, which is based on the principles of moving target defense, utilizes a stochastic switching structure to dynamically and continuously alter the parameters of the system, while hindering the attacker's ability to conduct successful reconnaissance to the system. The unpredictability of the current actuator and sensor is optimized using an information entropy measure, which is induced by probabilistic switching. The reactive mechanism on the other side, detects potentially attacked components, namely sensors and actuators, by leveraging online data to compute an integral Bellman error. A rigorous mathematical framework is presented to guarantee the stability of the equilibrium point of the closed-loop system, and provide a quantified bound on the performance loss when utilizing both reactive and proactive mechanisms. Simulation results show the efficacy of the proposed approaches on a benchmark aircraft model.