Peng Li

Daqing Oilfield General Hospital

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Trends

Author Order

Document Type

Co-Authors

Zhuo Feng

Southwest Jiaotong University

Wenrui Zhang

Harbin Normal University

Guoyao Wu

Texas A&M University

Yongtae Kim

Kyungpook National University

Xiaoji Ye

Guangdong Academy of Sciences

Garng M. Huang

Texas A&M University

Wei Dong

Harbin Institute of Technology

Jiang Hu

Texas A&M University

Guo Yu

Xiangtan University

Wengu Chen

Institute of Applied Physics and Computational Mathematics

Cooperative Institutions

Texas A&M University

Chinese Academy of Sciences

Mitchell Institute

Tsinghua University

Peking University

University of California, Santa Barbara

University of Electronic Science and Technology of China

Zhejiang University

Harbin Institute of Technology

Huazhong University of Science and Technology

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Field

Performance and robustness of bio-inspired digital liquid state machines: A case study of speech recognition

Neurocomputing (2016)

Yingyezhe Jin Peng Li

Robustness

Leverage (statistics)

10.1016/j.neucom.2016.11.045

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Citations (32)

A Joint Code-Doppler Acquisition Algorithm for DSSS-MSK Signal Based on Compressed Sensing

Peng Li Xiaoqi Yuan Renhong Xie Weiwei Zhu Yibin Rui

The unavoidable Doppler offset significantly reduces the correlation peak for code acquisition of the direct sequence spread spectrum minimum shift keying (DSSS-MSK) signal in highly dynamic environment. To eliminate the severe influence caused by the Doppler offset, a novel joint code-Doppler acquisition method based on compressed sensing (CS) is proposed. This acquisition algorithm introduces CS to spread spectrum communication. A sparse dictionary is constructed based on the observed signal, which is then reconstructed in the sparse domain by orthogonal matching pursuit (OMP) to obtain the code delay and Doppler offset simultaneously. However, the computational complexity of this algorithm is very high over a wide searching range. To reduce the computational complexity and improve this algorithm further, a two-stage acquisition method is introduced. Simulation results show that the detection probability of the proposed method reaches 0.9 in a low signal to noise ratio (SNR) at a -20dB environment.

SIGNAL (programming language)

10.1145/3163080.3163083

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Citations (1)

Parallel On-Chip Power Distribution Network Analysis on Multi-Core-Multi-GPU Platforms

IEEE Transactions on Very Large Scale Integration (VLSI) Systems (2010)

Zhuo Feng Zhiyu Zeng Peng Li

The challenging task of analyzing on-chip power (ground) distribution networks with multimillion node complexity and beyond is key to today's large chip designs. For the first time, we show how to exploit recent massively parallel single-instruction multiple-thread (SIMT)-based graphics processing unit (GPU) platforms to tackle large-scale power grid analysis with promising performance. Several key enablers including GPU-speciflc algorithm design, circuit topology transformation, workload partitioning, performance tuning are embodied in our GPU-accelerated hybrid multigrid (HMD) algorithm (GpuHMD) and its implementation. We also demonstrate that using the HMD solver as a preconditioner, the conjugate gradient solver can converge much faster to the true solution with good robustness. Extensive experiments on industrial and synthetic benchmarks have shown that for DC power grid analysis using one GPU, the proposed simulation engine achieves up to 100× runtime speedup over a state-of-the-art direct solver and more than 50× speedup over the CPU based multigrid implementation, while utilizing a four-core-four-GPU system, a grid with eight million nodes can be solved within about 1 s. It is observed that the proposed approach scales favorably with the circuit complexity, at a rate about 1 s per two million nodes on a single GPU card.

Speedup

Graphics processing unit

Multi-core processor

Solver

Design space exploration

Multigrid method

10.1109/tvlsi.2010.2059718

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Citations (40)

Reduced order modeling of passive and quasi-active dendrites for nervous system simulation

Journal of Computational Neuroscience (2011)

Boyuan Yan Peng Li

Biological neuron model

Theory of computation

Dendritic spike

Dendrite (mathematics)

Complex system

10.1007/s10827-010-0309-5

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Citations (8)

Computational Studies of Novel Aniline Pyrimidine WDR5‐MLL1 Inhibitors: QSAR, Molecular Docking, and Molecular Dynamics Simulation

ChemistrySelect (2024)

Guo Li Chun Ying Wang Jian Bo Tong Peng Li Peng Gao

Abstract WD repeat‐containing protein 5 (WDR5) and mixed lineage leukemia (MLL) are critical for maintaining tumorigenesis in human cancers. Disruption of the MLL1–WDR5 interaction has been viewed as a promising therapeutic strategy for the treatment of leukemia. Here, we report a series of protein–protein binding interaction modes targeting MLL1‐WDR5 inhibitors using 3D‐QSAR, and molecular docking. CoMFA with q 2 = 0.724, r 2 = 0.957, r pred 2 = 0.874 and CoMSIA with q 2 = 0.826, r 2 = 0.995, r pred 2 = 0.827. Topomer CoMFA results show q 2 = 0.807, r 2 = 0.976, r pred 2 = 0.789, and HQSAR results show q 2 = 0.873, r 2 = 0.982, r pred 2 = 0.794. The 3D‐QSAR model with high validation and prediction performance is successfully constructed. Contour maps and molecular docking results according to the four models, 26 new compounds are finally designed on the computer after molecular screening. Further molecular dynamics simulations (MD) of compounds G01, G04, G09, G43, and G44 with high predicted activity are carried out to explore their possible binding modes. ILE305, PHE133, CYS261, and ALA176 are found to play crucial roles in stabilizing the inhibitor. ADMET predictions are also performed for these 26 new compounds. These results serve as references for the design of effective WDR5‐MLL1 inhibitors in the future.

Docking (animal)

Molecular model

10.1002/slct.202402567

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Citations (0)

RF BIST for ADPLL-based polar transmitters with wide-band DCO gain calibration

Leyi Yin Peng Li

We propose a BIST scheme to provide online testing for all-digital PLL based polar transmitters. Multiple digital signatures are collected by adding a branch digital filter optimized to increase the sensitivities to non-idealities. The test signatures are processed using simple digital processing to provide an estimate for error vector magnitude (EVM), a key RF performance measure for the transmitter. Additionally a digital self-calibration scheme is proposed to eliminate the EVM degradation due to large wide-band DCO gain mismatch. We show that a proper exploration of digital implementation style is instrumental for facilitating novel low-cost built-in test and calibration solutions for mixed-signal and RF applications.

Degradation

10.1109/isqed.2011.5770741

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Citations (0)

Binary Neural Networks as a general-propose compute paradigm for on-device computer vision

arXiv (Cornell University) (2022)

Guhong Nie Lirui Xiao Menglong Zhu Dongliang Chu Yue Shen

For binary neural networks (BNNs) to become the mainstream on-device computer vision algorithm, they must achieve a superior speed-vs-accuracy tradeoff than 8-bit quantization and establish a similar degree of general applicability in vision tasks. To this end, we propose a BNN framework comprising 1) a minimalistic inference scheme for hardware-friendliness, 2) an over-parameterized training scheme for high accuracy, and 3) a simple procedure to adapt to different vision tasks. The resultant framework overtakes 8-bit quantization in the speed-vs-accuracy tradeoff for classification, detection, segmentation, super-resolution and matching: our BNNs not only retain the accuracy levels of their 8-bit baselines but also showcase 1.3-2.4$\times$ faster FPS on mobile CPUs. Similar conclusions can be drawn for prototypical systolic-array-based AI accelerators, where our BNNs promise 2.8-7$\times$ fewer execution cycles than 8-bit and 2.1-2.7$\times$ fewer cycles than alternative BNN designs. These results suggest that the time for large-scale BNN adoption could be upon us.

Speedup

10.48550/arxiv.2202.03716

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Citations (3)

Variational analysis of large power grids by exploring statistical sampling sharing and spatial locality

International Conference on Computer Aided Design (2005)

Peng Li

We propose a parametric random walk algorithm to facilitate a feasible evaluation of a few critical network nodes under the influence of a large number of variation sources in a power grid. By combining statistical sampling sharing with random walks, we devise an efficient localized sensitivity analysis for large power distribution networks such that the analysis can be conducted without solving the complete network. We further show that this sampling-based parametric analysis can be extended from the first order sensitivity analysis to a more accurate second order analysis. By exploiting the natural spatial locality inherent in our algorithm formulation, the second order parametric analysis can be conducted very efficiently even for a large number of global and local variation sources. The proposed approach is demonstrated by analyzing large power grids under the influence of process and current loading variations to which the application of the standard brutal-force circuit simulation becomes completely infeasible. Our results have demonstrated the superior performance of the proposed algorithm both in terms of accuracy and runtime.

10.5555/1129601.1129694

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Citations (18)

Using Presilicon Knowledge to Excite Nonlinear Failure Modes in Large Mixed-Signal Circuits

IEEE Design and Test (2016)

Parijat Mukherjee Peng Li

With the continuous growing of analog circuit complexity, identifying critical failure modes is of great importance today. This article develops an information-theoretic framework to rank important parameters based on presilicon data. The proposed framework is further used to design the optimal test set that maximizes the probability of observing out-of-specification failures. A phase-locked loop example is adopted to validate the proposed framework.

Rank (graph theory)

SIGNAL (programming language)

10.1109/mdat.2016.2593399

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Citations (0)

Bivariate Quasi-Tower Sets and Their Associated Lagrange Interpolation Bases

arXiv (Cornell University) (2010)

Tian Dong Xiaoying Wang Shugong Zhang Peng Li

As we all known, there is still a long way for us to solve arbitrary multivariate Lagrange interpolation in theory. Nevertheless, it is well accepted that theories about Lagrange interpolation on special point sets should cast important lights on the general solution. In this paper, we propose a new type of bivariate point sets, quasi-tower sets, whose geometry is more natural than some known point sets such as cartesian sets and tower sets. For bivariate Lagrange interpolation on quasi-tower sets, we construct the associated degree reducing interpolation monomial and Newton bases w.r.t. common monomial orderings theoretically. Moreover, by inputting these bases into Buchberger-Möller algorithm, we obtain the reduced Gröbner bases for vanishing ideals of quasi-tower sets much more efficiently than before.

Interpolation

Lagrange polynomial

10.48550/arxiv.1003.2493

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Citations (0)