A5005839811- Error Correcting Code Techniques
- VLSI and FPGA Design Techniques
- Mathematical Biology Tumor Growth
- Geometric Analysis and Curvature Flows
- Computational Fluid Dynamics and Aerodynamics
- Advanced Numerical Methods in Computational Mathematics
- Stochastic Gradient Optimization Techniques
- Mathematical and Theoretical Epidemiology and Ecology Models
- Navier-Stokes equation solutions
- Stochastic processes and financial applications
- Advanced Memory and Neural Computing
- COVID-19 epidemiological studies
- Numerical methods in inverse problems
- VLSI and Analog Circuit Testing
- Quantum Computing Algorithms and Architecture
- Ferroelectric and Negative Capacitance Devices
- Advancements in Photolithography Techniques
- Low-power high-performance VLSI design
- Advanced battery technologies research
- Parallel Computing and Optimization Techniques
- Polyamine Metabolism and Applications
- 3D IC and TSV technologies
- Numerical methods for differential equations
- Neural Networks and Reservoir Computing
- Corporate Finance and Governance
ShanghaiTech University
2022-2025
Chinese University of Hong Kong
2021-2025
Xidian University
2022-2025
First Affiliated Hospital of Fujian Medical University
2025
Fujian Medical University
2025
University of California, Los Angeles
2020-2024
China University of Geosciences
2024
Xi’an Children’s Hospital
2024
Guangzhou University
2024
Northeastern University
2024
Stochastic computation has recently been proposed for implementing artificial neural networks with reduced hardware and power consumption, but at a decreased accuracy processing speed. Most existing implementations are based on pre-training such that the weights predetermined neurons different layers, thus these lack ability to update values of network parameters. In this paper, stochastic computational multi-layer perceptron (SC-MLP) is by backward propagation algorithm updating layer...
Energy efficiency presents a significant challenge for stochastic computing (SC) due to the long random binary bit streams required accurate computation. In this paper, type of low discrepancy (LD) sequences, Sobol sequence, is considered energy-efficient implementations SC circuits. The use sequences improves output accuracy circuit with reduced sequence length compared another LD Halton and conventional linear feedback shift register (LFSR)-generated pseudorandom sequence. leads similar or...
The coronavirus disease 2019 (COVID-19) pandemic is changing and impacting lives on a global scale. In this paper, we introduce mean-field game model in controlling the propagation of epidemics spatial domain. control variable, velocity, first introduced for classical models, such as SIR model. For proposed model, provide fast numerical algorithms based proximal primal-dual methods. Numerical experiments demonstrate that illustrates how to separate infected patients domain effectively.
As the most common nonlinear optical process, second harmonic generation (SHG) has important application value in field of nanophotonics. With rapid development metal nanomaterial processing and chemical preparation technology, various structures based on nanoparticles have been used to achieve enhancement modulation SHG. In optics, plasmonic nanostructures become potential candidates for optoelectronic devices because their highly adjustable physical characteristics. this article, first,...
Stochastic computing (SC) often requires long stochastic sequences and, thus, a latency to achieve accurate computation. The leads an inferior performance and low energy efficiency compared with most conventional binary designs. In this paper, type of low-discrepancy sequences, the Sobol sequence, is considered for use in SC. Compared pseudorandom generated by linear feedback shift registers (LFSRs), improves accuracy computation reduced sequence length. inherent feature generators enables...
Congestion modeling is crucial for enhancing the routability of VLSI placement solutions. The underutilization netlist information constrains efficacy existing layout-based congestion techniques. We devise a novel approach that grafts netlist-based message passing into model, thereby achieving better knowledge fusion between layout and to improve prediction performance. innovative heterogeneous message-passing paradigm more effectively incorporates routing demand model by considering...
ABSTRACT Lamotrigine is a commonly used anti‐seizure medication in pregnant women. However, its pharmacokinetics (PK) during pregnancy markedly change, increasing the frequency of seizures and endangering safety mother fetus. Meanwhile, insufficient PK data on lamotrigine hinders dose adjustment. This study aimed to predict maternal fetal provide recommendations for A physiologically based pharmacokinetic (PBPK) model was constructed using PK‐Sim MoBi validated with clinical data. The area...
This paper presents a mechanistic study on the synthesis of Janus Mn:Ag2Se-Au heterogeneous nanostructures based step-by-step protocol. The extinction spectra and morphological observations exhibit interrelated data that elucidate...
Placement and routing (PnR) is the most time-consuming part of physical design flow. Recognizing performance ahead time can assist designers tools to optimize placement results in advance. In this paper, we propose a fully convolutional network model predict congestion hotspots then incorporate prediction into engine, DREAMPlace, get more route-friendly result. The experimental on ISPD2015 benchmarks show that with superior accuracy model, our proposed approach achieve up 9.05% reduction...
Related DatabasesWeb of Science You must be logged in with an active subscription to view this.Article DataHistorySubmitted: 30 April 2020Accepted: 13 July 2021Published online: 11 October 2021Keywordsmean field games, kernel methods, multiagent systems, optimal control, Fourier methodsAMS Subject HeadingsPrimary, 35Q89, 49N80, 35A15, 65M70, 93A16; Secondary, 35Q91, 35Q93, 93A15Publication DataISSN (print): 0036-1429ISSN (online): 1095-7170Publisher: Society for Industrial and Applied...
Gradient descent (GD) is a widely used optimization algorithm in machine learning. In this paper, novel stochastic computing GD circuit (SC-GDC) proposed by encoding the gradient information sequences. Inspired structure of neuron, integrator to optimize weights learning its "inhibitory" and "excitatory" inputs. Specifically, two AND (or XNOR) gates for unipolar representation bipolar representation) one are, respectively, implement multiplications accumulations algorithm. Thus, SC-GDC very...
High-level synthesis (HLS) tools have gained great attention in recent years because it emancipates engineers from the complicated and heavy hardware description language writing facilitates implementations of modern applications (e.g., deep learning models) on Field-programmable Gate Array (FPGA) , by using high-level languages HLS directives. However, finding good directives is challenging, due to time-consuming design processes, balances among different objectives, diverse fidelities...