arXiv subjects

Jingfang Pei

Publications and source records attributed to Jingfang Pei.

7 recordsLinked to original sources

arXiv2024

Hardware implementation of timely reliable Bayesian decision-making using memristors

Brains perform decision-making by Bayes theorem. The theorem quantifies events as probabilities and, based on probability rules, renders the decisions. Learning from this, Bayes theorem can be applied to enable efficient user-scene interactions. However, given the probabilistic nature, implementing Bayes theorem in hardware using conventional deterministic computing can incur excessive computational cost and decision latency. Though challenging, here we present a probabilistic computing approach based on memristors to implement the Bayes theorem. We integrate memristors with Boolean logics and, by exploiting the volatile stochastic switching of the memristors, realise probabilistic logic operations, key for hardware Bayes theorem implementation. To empirically validate the efficacy of the hardware Bayes theorem in user-scene interactions, we develop lightweight Bayesian inference and fusion hardware operators using the probabilistic logics and apply the operators in road scene parsing for self-driving, including route planning and obstacle detection. The results show our operators can achieve reliable decisions in less than 0.4 ms (or equivalently 2,500 fps), outperforming human decision-making and the existing driving assistance systems.

cs.LG↗

arXiv2024

GEM: A GEneral Memristive Transistor Model

Neuromorphic devices, with their distinct advantages in energy efficiency and parallel processing, are pivotal in advancing artificial intelligence applications. Among these devices, memristive transistors have attracted significant attention due to their superior stability and operation flexibility compared to two-terminal memristors. However, the lack of a robust model that accurately captures their complex electrical behavior has hindered further exploration of their potential. In this work, we introduce the GEneral Memristive transistor (GEM) model to address this challenge. The GEM model incorporates time-dependent differential equation, a voltage-controlled moving window function, and a nonlinear current output function, enabling precise representation of both switching and output characteristics in memristive transistors. Compared to previous models, the GEM model demonstrates a 300% improvement in modeling the switching behavior, while effectively capturing the inherent nonlinearities and physical limits of these devices. This advancement significantly enhances the realistic simulation of memristive transistors, thereby facilitating further exploration and application development.

physics.app-ph↗

arXiv2024

Harnessing physical entropy noise in structurally metastable 1T' molybdenum ditelluride for true random number generation

True random numbers are essential in various research and engineering problems. Their generation depends upon a robust physical entropy noise. Here, we present true random number generation by harnessing the conductance noise probed in structurally metastable 1T' molybdenum ditelluride (MoTe2). The noise, well-fitting a Poisson process, is proved a robust physical entropy noise at low and even cryogenic temperatures. Noise characteristic analysis suggests the noise may originate from the polarization variations of the underlying ferroelectric dipoles in 1T' MoTe2. We demonstrate the noise allows for true random number generation, enabling their use as seed for generating high-throughput secure random numbers exceeding 1 Mbit/s, appealing for practical applications in, for instance, cryptography where data security is now a severe issue. As an example, we show biometric information safeguarding in neural networks by using the random numbers as mask, proving a promising data security measure in big data and artificial intelligence.

cs.CR↗

arXiv2024

Analysis on reservoir activation with the nonlinearity harnessed from solution-processed molybdenum disulfide

Reservoir computing is a recurrent neural network designed for approximating complex dynamics in, for instance, motion tracking, spatial-temporal pattern recognition, and chaotic attractor reconstruction. Its implementation demands intense computation for the nonlinear transformation of the reservoir input, i.e. activating the reservoir. Configuring physical nonlinear networks as the reservoir and employing the physical nonlinearity for the reservoir activation is an emergent solution to address the challenge. In this work, we analyze the feasibility of harnessing the nonlinearity from solution-processed molybdenum disulfide (MoS2) for reservoir activation. We fit the high-order nonlinearity, achieved by Stark modulation of MoS2, as the activation function to facilitate implementation of a reservoir computing model. Due to the high-order nonlinearity, the model can achieve long-term synchronization and robust generalization for complex dynamical system regression. As a potential application exploring this ability, we appoint the model to generate chaotic random numbers for secure data encryption. Given this reservoir activation capability, and the scalability of solution-processed MoS2, our results suggest the potential for realizing physical reservoir computing with solution-processed MoS2.

physics.app-ph↗

arXiv2024

Lightweight, error-tolerant edge detection using memristor-enabled stochastic logics

The demand for efficient edge vision has spurred the interest in developing stochastic computing approaches for performing image processing tasks. Memristors with inherent stochasticity readily introduce probability into the computations and thus enable stochastic image processing computations. Here, we present a stochastic computing approach for edge detection, a fundamental image processing technique, facilitated with memristor-enabled stochastic logics. Specifically, we integrate the memristors with logic circuits and harness the stochasticity from the memristors to realize compact stochastic logics for stochastic number encoding and processing. The stochastic numbers, exhibiting well-regulated probabilities and correlations, can be processed to perform logic operations with statistical probabilities. This can facilitate lightweight stochastic edge detection for edge visual scenarios characterized with high-level noise errors. As a practical demonstration, we implement a hardware stochastic Roberts cross operator using the stochastic logics, and prove its exceptional edge detection performance, remarkably, with 95% less computational cost while withstanding 50% bit-flip errors. The results underscore the great potential of our stochastic edge detection approach in developing lightweight, error-tolerant edge vision hardware and systems for autonomous driving, virtual/augmented reality, medical imaging diagnosis, industrial automation, and beyond.

cs.ET↗

arXiv2024

Macroscopic electro-optical modulation of solution-processed molybdenum disulfide

Molybdenum disulfide (MoS2) has drawn great interest for tunable photonics and optoelectronics advancement. Its solution processing, though scalable, results in randomly networked ensembles of discrete nanosheets with compromised properties for tunable device fabrication. Here, we show via density-functional theory calculations that the electronic structure of the individual solution-processed nanosheets can be modulated by external electric fields collectively. Particularly, the nanosheets can form Stark ladders, leading to variations in the underlying optical transition processes and thus, tunable macroscopic optical properties of the ensembles. We experimentally confirm the macroscopic electro-optical modulation employing solution-processed thin-films of MoS2 and ferroelectric P(VDF-TrFE), and prove that the localized polarization fields of P(VDF-TrFE) can modulate the optical properties of MoS2, specifically, the optical absorption and photoluminescence on a macroscopic scale. Given the scalability of solution processing, our results underpin the potential of electro-optical modulation of solution-processed MoS2 for scalable tunable photonics and optoelectronics. As an illustrative example, we successfully demonstrate solution-processed electro-absorption modulators.

cond-mat.mtrl-sci↗

arXiv2023

Conduction modulation of solution-processed two-dimensional materials

Solution-processed two-dimensional (2D) materials hold promise for their scalable applications. However, the random, fragmented nature of the solution-processed nanoflakes and the poor percolative conduction through their discrete networks limit the performance of the enabled devices. To overcome the problem, we report conduction modulation of the solution-processed 2D materials via the Stark effect. Using liquid-phase exfoliated molybdenum disulfide (MoS2) as an example, we demonstrate nonlinear conduction modulation with a switching ratio of >105 by the local fields from the interfacial ferroelectric P(VDF-TrFE). Through density-functional theory calculations and in situ Raman scattering and photoluminescence spectroscopic analysis, we understand the modulation arises from a charge redistribution in the solution-processed MoS2. Beyond MoS2, we show the modulation may be viable for the other solution-processed 2D materials and low-dimensional materials. The effective modulation can open their electronic device applications.

physics.app-ph↗