arXiv subjects

Xing Chen

Publications and source records attributed to Xing Chen.

At least 73 records · Page 4Linked to original sources

arXiv2022

Profile-Guided Parallel Task Extraction and Execution for Domain Specific Heterogeneous SoC

In this study, we introduce a methodology for automatically transforming user applications in the radar and communication domain written in C/C++ based on dynamic profiling to a parallel representation targeted for a heterogeneous SoC. We present our approach for instrumenting the user application binary during the compilation process with barrier synchronization primitives that enable runtime system schedule and execute independent tasks concurrently over the available compute resources. We demonstrate the capabilities of our integrated compile time and runtime flow through task-level parallel and functionally correct execution of real-life applications. We perform validation of our integrated system by executing four distinct applications each carrying various degrees of task level parallelism over the Xeon-based multi-core homogeneous processor. We use the proposed compilation and code transformation methodology to re-target each application for execution on a heterogeneous SoC composed of three ARM cores and one FFT accelerator that is emulated on the Xilinx Zynq UltraScale+ platform. We demonstrate our runtime's ability to process application binary, dispatch independent tasks over the available compute resources of the emulated SoC on the Zynq FPGA based on three different scheduling heuristics. Finally we demonstrate execution of each application individually with task level parallelism on the Zynq FPGA and execution of workload scenarios composed of multiple instances of the same application as well as mixture of two distinct applications to demonstrate ability to realize both application and task level parallel execution. Our integrated approach offers a path forward for application developers to take full advantage of the target SoC without requiring users to become hardware and parallel programming experts.

cs.DC↗

arXiv2022

Symmetric Saliency-based Adversarial Attack To Speaker Identification

Adversarial attack approaches to speaker identification either need high computational cost or are not very effective, to our knowledge. To address this issue, in this paper, we propose a novel generation-network-based approach, called symmetric saliency-based encoder-decoder (SSED), to generate adversarial voice examples to speaker identification. It contains two novel components. First, it uses a novel saliency map decoder to learn the importance of speech samples to the decision of a targeted speaker identification system, so as to make the attacker focus on generating artificial noise to the important samples. It also proposes an angular loss function to push the speaker embedding far away from the source speaker. Our experimental results demonstrate that the proposed SSED yields the state-of-the-art performance, i.e. over 97% targeted attack success rate and a signal-to-noise level of over 39 dB on both the open-set and close-set speaker identification tasks, with a low computational cost.

cs.SD↗

arXiv2022

A Cost-Driven Fuzzy Scheduling Strategy for Intelligent Workflow Decision Making Systems in Uncertain Edge-Cloud Environments

Workflow decision making is critical to performing many practical workflow applications. Scheduling in edge-cloud environments can address the high complexity problem of workflow applications, while decreasing the data transmission delay between the cloud and end devices. However, because of the heterogeneous resources in edge-cloud environments and the complicated data dependencies among the tasks in a workflow, significant challenges for workflow scheduling remain, including the selection of an optimal tasks-servers solution from the possible numerous combinations. The existing studies have been mainly done subject to rigorous conditions without fluctuations, ignoring the fact that workflow scheduling is typically present in uncertain environments. In this study, we focus on reducing the execution cost of workflow applications mainly caused by task computation and data transmission, while satisfying the workflow deadline in uncertain edge-cloud environments. The Triangular Fuzzy Numbers (TFNs) are adopted to represent the task processing time and data transferring time. A cost-driven fuzzy scheduling strategy based on an Adaptive Discrete Particle Swarm Optimization (ADPSO) algorithm is proposed, which employs the operators of Genetic Algorithm (GA). This strategy introduces the randomly two-point crossover operator, neighborhood mutation operator, and adaptive multipoint mutation operator of GA to effectively avoid converging on local optima. The experimental results show that our strategy can effectively reduce the workflow execution cost in uncertain edge-cloud environments, compared with other benchmark solutions.

cs.DC↗

arXiv2022

An Adjustable Farthest Point Sampling Method for Approximately-sorted Point Cloud Data

Sampling is an essential part of raw point cloud data processing such as in the popular PointNet++ scheme. Farthest Point Sampling (FPS), which iteratively samples the farthest point and performs distance updating, is one of the most popular sampling schemes. Unfortunately it suffers from low efficiency and can become the bottleneck of point cloud applications. We propose adjustable FPS (AFPS), parameterized by M, to aggressively reduce the complexity of FPS without compromising on the sampling performance. Specifically, it divides the original point cloud into M small point clouds and samples M points simultaneously. It exploits the dimensional locality of an approximately sorted point cloud data to minimize its performance degradation. AFPS method can achieve 22 to 30x speedup over original FPS. Furthermore, we propose the nearest-point-distance-updating (NPDU) method to limit the number of distance updates to a constant number. The combined NPDU on AFPS method can achieve a 34-280x speedup on a point cloud with 2K-32K points with algorithmic performance that is comparable to the original FPS. For instance, for the ShapeNet part segmentation task, it achieves 0.8490 instance average mIoU (mean Intersection of Union), which is only 0.0035 drop compared to the original FPS.

cs.CV↗

arXiv2022

ResSFL: A Resistance Transfer Framework for Defending Model Inversion Attack in Split Federated Learning

This work aims to tackle Model Inversion (MI) attack on Split Federated Learning (SFL). SFL is a recent distributed training scheme where multiple clients send intermediate activations (i.e., feature map), instead of raw data, to a central server. While such a scheme helps reduce the computational load at the client end, it opens itself to reconstruction of raw data from intermediate activation by the server. Existing works on protecting SFL only consider inference and do not handle attacks during training. So we propose ResSFL, a Split Federated Learning Framework that is designed to be MI-resistant during training. It is based on deriving a resistant feature extractor via attacker-aware training, and using this extractor to initialize the client-side model prior to standard SFL training. Such a method helps in reducing the computational complexity due to use of strong inversion model in client-side adversarial training as well as vulnerability of attacks launched in early training epochs. On CIFAR-100 dataset, our proposed framework successfully mitigates MI attack on a VGG-11 model with a high reconstruction Mean-Square-Error of 0.050 compared to 0.005 obtained by the baseline system. The framework achieves 67.5% accuracy (only 1% accuracy drop) with very low computation overhead. Code is released at: https://github.com/zlijingtao/ResSFL.

cs.LG↗

arXiv2022

Self-testing randomness from a nuclear spin system

Randomness is a very important resource for cryptography, algorithms, and scientific simulations. Since all classical processes are considered to be intrinsically deterministic, we must build quantum random number generators which utilize quantum processes to generate true randomness. Quantum random number generators have been realized in different quantum systems, including quantum optical systems, and trapped ions. Here we present a proof-of-concept random number generator based on a nuclear spin system for the first time. The state preparation and measurements are performed with high-fidelity operations in our system. The entropy of randomness in the experimental data is quantified by two dimension witness certification protocols, which require no detailed models to describe the experimental devices but only some general assumptions, such as the limited dimensionality and the independence of the experimental devices.

quant-ph↗

arXiv2022

Secure two-way fiber-optic time transfer against sub-ns asymmetric delay attack

Two-way fiber-optic time transfer is a promising precise time synchronization technique with sub-nanosecond accuracy. However, asymmetric delay attack is a serious threat which cannot be prevent by any encryption method. In this paper, a dynamic model based scheme is proposed to defense the sub-nanosecond asymmetric delay attack. A threshold is set according to the estimated time difference by a two-state clock model where the fixed frequency difference is excluded from the time difference to detect the asymmetric delay attack which is smaller than the time difference induced by the fixed frequency difference. Theoretical simulation and experimental demonstration are implemented to prove the feasibility of the scheme. A two-way fiber-optic time transfer system with time stability with 24.5ps, 3.98ps, and 2.95ps at 1s, 10s, and 100s averaging time is shown under sub-ns asymmetric time delay attack experimentally. The proposed method provides a promising secure sub-ns precise time synchronization technique against asymmetric delay attack.

quant-ph↗

arXiv2021

Forecasting the outcome of spintronic experiments with Neural Ordinary Differential Equations

Deep learning has an increasing impact to assist research, allowing, for example, the discovery of novel materials. Until now, however, these artificial intelligence techniques have fallen short of discovering the full differential equation of an experimental physical system. Here we show that a dynamical neural network, trained on a minimal amount of data, can predict the behavior of spintronic devices with high accuracy and an extremely efficient simulation time, compared to the micromagnetic simulations that are usually employed to model them. For this purpose, we re-frame the formalism of Neural Ordinary Differential Equations (ODEs) to the constraints of spintronics: few measured outputs, multiple inputs and internal parameters. We demonstrate with Spin-Neural ODEs an acceleration factor over 200 compared to micromagnetic simulations for a complex problem -- the simulation of a reservoir computer made of magnetic skyrmions (20 minutes compared to three days). In a second realization, we show that we can predict the noisy response of experimental spintronic nano-oscillators to varying inputs after training Spin-Neural ODEs on five milliseconds of their measured response to different excitations. Spin-Neural ODE is a disruptive tool for developing spintronic applications in complement to micromagnetic simulations, which are time-consuming and cannot fit experiments when noise or imperfections are present. Spin-Neural ODE can also be generalized to other electronic devices involving dynamics.

cs.LG↗

arXiv2021

Tsformer: Time series Transformer for tourism demand forecasting

AI-based methods have been widely applied to tourism demand forecasting. However, current AI-based methods are short of the ability to process long-term dependency, and most of them lack interpretability. The Transformer used initially for machine translation shows an incredible ability to long-term dependency processing. Based on the Transformer, we proposed a time series Transformer (Tsformer) with Encoder-Decoder architecture for tourism demand forecasting. The proposed Tsformer encodes long-term dependency with encoder, captures short-term dependency with decoder, and simplifies the attention interactions under the premise of highlighting dominant attention through a series of attention masking mechanisms. These improvements make the multi-head attention mechanism process the input sequence according to the time relationship, contributing to better interpretability. What's more, the context processing ability of the Encoder-Decoder architecture allows adopting the calendar of days to be forecasted to enhance the forecasting performance. Experiments conducted on the Jiuzhaigou valley and Siguniang mountain tourism demand datasets with other nine baseline methods indicate that the proposed Tsformer outperformed all baseline models in the short-term and long-term tourism demand forecasting tasks. Moreover, ablation studies demonstrate that the adoption of the calendar of days to be forecasted contributes to the forecasting performance of the proposed Tsformer. For better interpretability, the attention weight matrix visualization is performed. It indicates that the Tsformer concentrates on seasonal features and days close to days to be forecast in short-term forecasting.

cs.LG↗

arXiv2021

Communication and Computation Reduction for Split Learning using Asynchronous Training

Split learning is a promising privacy-preserving distributed learning scheme that has low computation requirement at the edge device but has the disadvantage of high communication overhead between edge device and server. To reduce the communication overhead, this paper proposes a loss-based asynchronous training scheme that updates the client-side model less frequently and only sends/receives activations/gradients in selected epochs. To further reduce the communication overhead, the activations/gradients are quantized using 8-bit floating point prior to transmission. An added benefit of the proposed communication reduction method is that the computations at the client side are reduced due to reduction in the number of client model updates. Furthermore, the privacy of the proposed communication reduction based split learning method is almost the same as traditional split learning. Simulation results on VGG11, VGG13 and ResNet18 models on CIFAR-10 show that the communication cost is reduced by 1.64x-106.7x and the computations in the client are reduced by 2.86x-32.1x when the accuracy degradation is less than 0.5% for the single-client case. For 5 and 10-client cases, the communication cost reduction is 11.9x and 11.3x on VGG11 for 0.5% loss in accuracy.

cs.LG↗

arXiv2021

Phase Noise and Frequency Accuracy in Crystal-less Wireless Edge Nodes

This paper presents a fundamental analysis connecting phase noise and long-term frequency accuracy of oscillators and explores the possibilities and limitations in crystal-less frequency calibration for wireless edge nodes from a noise-impact perspective. N-period-average jitter (NPAJ) is introduced as a link between the spectral characterization of phase noise and long-term frequency accuracy. It is found that flicker noise or other colored noise profiles coming from the reference in a frequency synthesizer is the dominant noise source affecting long-term frequency accuracy. An average processing unit embedded in an ADPLL is proposed based on the N-period-average jitter concept to enhance frequency accuracy in a Calibrate and Open-loop scenario commonly used in low power radios. With this low-cost block, the frequency calibration accuracy can be directly associated with the reference noise performance. Thus, the feasibility of XO-less design with certain communication standards can be easily evaluated with the proposed theory.

eess.SP↗

arXiv2021

Certified Randomness from Remote State Preparation Dimension Witness

Randomness in Bell test data can be device-independently certified by Bell's theorem without placing assumptions about the experimental devices. The device-independent randomness has very demanding requirement about the experimental devices and relatively lower output randomness. With the same Bell test data we can extract substantially more randomness without using Bell's theorem. To achieve this goal, we introduce a remote state preparation dimension witness and a semi-device-independent randomness certification model which is based on it. This is one important step towards practical use of Bell test in randomness generation.

quant-ph↗

arXiv2020

Revenue and Energy Efficiency-Driven Delay Constrained Computing Task Offloading and Resource Allocation in a Vehicular Edge Computing Network: A Deep Reinforcement Learning Approach

For in-vehicle application,task type and vehicle state information, i.e., vehicle speed, bear a significant impact on the task delay requirement. However, the joint impact of task type and vehicle speed on the task delay constraint has not been studied, and this lack of study may cause a mismatch between the requirement of the task delay and allocated computation and wireless resources. In this paper, we propose a joint task type and vehicle speed-aware task offloading and resource allocation strategy to decrease the vehicl's energy cost for executing tasks and increase the revenue of the vehicle for processing tasks within the delay constraint. First, we establish the joint task type and vehicle speed-aware delay constraint model. Then, the delay, energy cost and revenue for task execution in the vehicular edge computing (VEC) server, local terminal and terminals of other vehicles are calculated. Based on the energy cost and revenue from task execution,the utility function of the vehicle is acquired. Next, we formulate a joint optimization of task offloading and resource allocation to maximize the utility level of the vehicles subject to the constraints of task delay, computation resources and wireless resources. To obtain a near-optimal solution of the formulated problem, a joint offloading and resource allocation based on the multi-agent deep deterministic policy gradient (JORA-MADDPG) algorithm is proposed to maximize the utility level of vehicles. Simulation results show that our algorithm can achieve superior performance in task completion delay, vehicles' energy cost and processing revenue.

cs.MM↗

arXiv2020

Direct Visualization of Irreducible Ferrielectricity in Crystals

In solids, charge polarity can one-to-one correspond to spin polarity phenomenologically, e.g. ferroelectricity/ferromagnetism, antiferroelectricity/antiferromagnetism, and even dipole-vortex/magnetic-vortex, but ferrielectricity/ferrimagnetism kept telling a disparate story in microscopic level. Since the definition of a charge dipole involves more than one ion, there may be multiple choices for a dipole unit, which makes most ferrielectric orders equivalent to ferroelectric ones, i.e. this ferrielectricity is not necessary to be a real independent branch of polarity. In this work, by using the spherical aberration-corrected scanning transmission electron microscope, we visualize a nontrivial ferrielectric structural evolution in BaFe2Se3, in which the development of two polar sub-lattices is out-of-sync, for which we term it as irreducible ferrielectricity. Such irreducible ferrielectricity leads to a non-monotonic behavior for the temperature-dependent polarization, and even a compensation point in the ordered state. Our finding unambiguously distinguishes ferrielectrics from ferroelectrics in solids.

cond-mat.mtrl-sci↗

arXiv2020

Single Photon Randomness based on a Defect Center in Diamond

The prototype of a quantum random number generator is a single photon which impinges onto a beam splitter and is then detected by single photon detectors at one of the two output paths. Prior to detection, the photon is in a quantum mechanical superposition state of the two possible outcomes with - ideally - equal amplitudes until its position is determined by measurement. When the two output modes are observed by a single photon detector, the generated clicks can be interpreted as ones and zeros - and a raw random bit stream is obtained. Here we implement such a random bit generator based on single photons from a defect center in diamond. We investigate the single photon emission of the defect center by an anti-bunching measurement. This certifies the "quantumness" of the supplied photonic input state, while the random "decision" is still based on the vacuum fluctuations at the open port of the beam-splitter. Technical limitations, such as intensity fluctuations, mechanical drift, and bias are discussed. A number of ways to suppress such unwanted effects, and an a priori entropy estimation are presented. The single photon nature allows for a characterization of the non-classicality of the source, and allows to determine a background fraction. Due to the NV-center's superior stability and optical properties, we can operate the generator under ambient conditions around the clock. We present a true 24/7 operation of the implemented random bit generator.

quant-ph↗

arXiv2019

Thermal Brownian Motion of Skyrmion for True Random Number Generation

The true random number generators (TRNGs) have received extensive attention because of their wide applications in information transmission and encryption. The true random numbers generated by TRNG are typically applied to the encryption algorithm or security protocol of the information security core. Recently, TRNGs have also been employed in emerging stochastic computing paradigm for reducing power consumption. Roughly speaking, TRNG can be divided into circuits-based, e.g., oscillator sampling or directly noise amplifying; and quantum physics-based, e.g., photoelectric effect. The former generally requires a large area and has a large power consumption, whereas the latter is intrinsic random but is more difficult to implement and usually requires additional post-processing circuitry. Very recently, magnetic skyrmion has become a promising candidate for implementing TRNG because of their nanometer size, high stability, and intrinsic thermal Brownian motion dynamics. In this work, we propose a TRNG based on continuous skyrmion thermal Brownian motion in a confined geometry at room temperature. True random bitstream can be easily obtained by periodically detecting the relative position of the skyrmion without the need for additional current pulses. More importantly, we implement a probability-adjustable TRNG, in which a desired ratio of 0 and 1 can be acquired by adding an anisotropy gradient through voltage-controlled magnetic anisotropy (VCMA) effect. The behaviors of the skyrmion-based TRNG are verified by using micromagnetic simulations. The National Institute of Standards and Technology (NIST) test results demonstrate that our proposed random number generator is TRNG with good randomness. Our research provides a new perspective for efficient TRNG realization.

physics.app-ph↗

arXiv2019

Hit Ratio Driven Mobile Edge Caching Scheme for Video on Demand Services

More and more scholars focus on mobile edge computing (MEC) technology, because the strong storage and computing capabilities of MEC servers can reduce the long transmission delay, bandwidth waste, energy consumption, and privacy leaks in the data transmission process. In this paper, we study the cache placement problem to determine how to cache videos and which videos to be cached in a mobile edge computing system. First, we derive the video request probability by taking into account video popularity, user preference and the characteristic of video representations. Second, based on the acquired request probability, we formulate a cache placement problem with the objective to maximize the cache hit ratio subject to the storage capacity constraints. Finally, in order to solve the formulated problem, we transform it into a grouping knapsack problem and develop a dynamic programming algorithm to obtain the optimal caching strategy. Simulation results show that the proposed algorithm can greatly improve the cache hit ratio.

cs.MM↗

arXiv2019

Cost-Driven Offloading for DNN-based Applications over Cloud, Edge and End Devices

Currently, deep neural networks (DNNs) have achieved a great success in various applications. Traditional deployment for DNNs in the cloud may incur a prohibitively serious delay in transferring input data from the end devices to the cloud. To address this problem, the hybrid computing environments, consisting of the cloud, edge and end devices, are adopted to offload DNN layers by combining the larger layers (more amount of data) in the cloud and the smaller layers (less amount of data) at the edge and end devices. A key issue in hybrid computing environments is how to minimize the system cost while accomplishing the offloaded layers with their deadline constraints. In this paper, a self-adaptive discrete particle swarm optimization (PSO) algorithm using the genetic algorithm (GA) operators was proposed to reduce the system cost caused by data transmission and layer execution. This approach considers the characteristics of DNNs partitioning and layers offloading over the cloud, edge and end devices. The mutation operator and crossover operator of GA were adopted to avert the premature convergence of PSO, which distinctly reduces the system cost through enhanced population diversity of PSO. The proposed offloading strategy is compared with benchmark solutions, and the results show that our strategy can effectively reduce the cost of offloading for DNN-based applications over the cloud, edge and end devices relative to the benchmarks.

cs.DC↗