Publications (47)
Continuous-time DC kernel --- a stable generalized first-order spline kernel
Tianshi Chen
The stable spline (SS) kernel and the diagonal correlated (DC) kernel are two kernels that have been applied and studied extensively for kernel-based regularized LTI system identif…
Linear Multiple Low-Rank Kernel Based Stationary Gaussian Processes Regression for Time Series
Feng Yin, Lishuo Pan, Xinwei He +4
Gaussian processes (GP) for machine learning have been studied systematically over the past two decades and they are by now widely used in a number of diverse applications. However…
Tutorial on Asymptotic Properties of Regularized Least Squares Estimator for Finite Impulse Response Model
Yue Ju, Tianshi Chen, Biqiang Mu +1
In this paper, we give a tutorial on asymptotic properties of the Least Square (LS) and Regularized Least Squares (RLS) estimators for the finite impulse response model with filter…
Distilling Object Detectors with Feature Richness
Zhixing Du, Rui Zhang, Ming Chang +4
In recent years, large-scale deep models have achieved great success, but the huge computational complexity and massive storage requirements make it a great challenge to deploy the…
Kernel-based Regularized Iterative Learning Control of Repetitive Linear Time-varying Systems
Xian Yu, Xiaozhu Fang, Biqiang Mu +1
For data-driven iterative learning control (ILC) methods, both the model estimation and controller design problems are converted to parameter estimation problems for some chosen mo…
On Input Design for Regularized LTI System Identification: Power-constrained Input
Biqiang Mu, Tianshi Chen
Input design is an important issue for classical system identification methods but has not been investigated for the kernel-based regularization method (KRM) until very recently. I…
Ex3: Automatic Novel Writing by Extracting, Excelsior and Expanding
Lei Huang, Jiaming Guo, Guanhua He +5
Generating long-term texts such as novels using artificial intelligence has always been a challenge. A common approach is to use large language models (LLMs) to construct a hierarc…
Kernel-Based Regularized Continuous-Time System Identification from Sampled Data
Xiaozhu Fang, Biqiang Mu, Tianshi Chen
The identification of continuous-time (CT) systems from discrete-time (DT) input and output signals, i.e., the sampled data, has received considerable attention for half a century.…
On Kernel Design for Regularized Non-Causal System Identification
Xiaozhu Fang, Tianshi Chen
Through one decade's development, the kernel-based regularization method (KRM) has become a complement to the classical maximum likelihood/prediction error method and an emerging n…
Hardwired-Neurons Language Processing Units as General-Purpose Cognitive Substrates
Yang Liu, Yi Chen, Yongwei Zhao +24
The rapid advancement of Large Language Models (LLMs) has established language as a core general-purpose cognitive substrate, driving the demand for specialized Language Processing…
Supplementary Material for CDC Submission No. 1461
Yue Ju, Tianshi Chen, Biqiang Mu +1
In this paper, we focus on the influences of the condition number of the regression matrix upon the comparison between two hyper-parameter estimation methods: the empirical Bayes (…
An Efficient Implementation for Spatial-Temporal Gaussian Process Regression and Its Applications
Junpeng Zhang, Yue Ju, Biqiang Mu +2
Spatial-temporal Gaussian process regression is a popular method for spatial-temporal data modeling. Its state-of-art implementation is based on the state-space model realization o…
Accelerated Sparse Bayesian Learning via Screening Test and Its Applications
Yiping Jiang, Tianshi Chen
In high-dimensional settings, sparse structures are critical for efficiency in term of memory and computation complexity. For a linear system, to find the sparsest solution provide…
The Noise Covariances of Linear Gaussian Systems with Unknown Inputs Are Not Uniquely Identifiable Using Autocovariance Least-squares
He Kong, Salah Sukkarieh, Travis J. Arnold +2
Existing works in optimal filtering for linear Gaussian systems with arbitrary unknown inputs assume perfect knowledge of the noise covariances in the filter design. This is imprac…
On Kernel Design for Regularized Volterra Series Identification of Wiener-Hammerstein Systems
Yu Xu, Biqiang Mu, Tianshi Chen
There have been increasing interests on the Volterra series identification with the kernel-based regularization method. The major difficulties are on the kernel design and efficien…
Asymptotic Theory for Regularized System Identification Part I: Empirical Bayes Hyper-parameter Estimator
Yue Ju, Biqiang Mu, Lennart Ljung +1
Regularized system identification is the major advance in system identification in the last decade. Although many promising results have been achieved, it is far from complete and…
DWM: A Decomposable Winograd Method for Convolution Acceleration
Di Huang, Xishan Zhang, Rui Zhang +9
Winograd's minimal filtering algorithm has been widely used in Convolutional Neural Networks (CNNs) to reduce the number of multiplications for faster processing. However, it is on…
The Impact of Mutation Rate on the Computation Time of Evolutionary Dynamic Optimization
Tianshi Chen, Yunji Chen, Ke Tang +2
Mutation has traditionally been regarded as an important operator in evolutionary algorithms. In particular, there have been many experimental studies which showed the effectivenes…
Maximum entropy properties of discrete-time first-order stable spline kernel
Tianshi Chen, Tohid Ardeshiri, Francesca P. Carli +3
The first order stable spline (SS-1) kernel is used extensively in regularized system identification. In particular, the stable spline estimator models the impulse response as a ze…
On Embeddings and Inverse Embeddings of Input Design for Regularized System Identification
Biqiang Mu, Tianshi Chen, He Kong +3
Input design is an important problem for system identification and has been well studied for the classical system identification, i.e., the maximum likelihood/prediction error meth…
On the Easiest and Hardest Fitness Functions
Jun He, Tianshi Chen, Xin Yao
The hardness of fitness functions is an important research topic in the field of evolutionary computation. In theory, the study can help understanding the ability of evolutionary a…
Maximum Entropy Property of Discrete-time Stable Spline Kernel
Tohid Ardeshiri, Tianshi Chen
In this paper, the maximum entropy property of the discrete-time first-order stable spline kernel is studied. The advantages of studying this property in discrete-time domain inste…
Maximum Entropy Kernels for System Identification
Francesca Paola Carli, Tianshi Chen, Lennart Ljung
A new nonparametric approach for system identification has been recently proposed where the impulse response is modeled as the realization of a zero-mean Gaussian process whose cov…
Kernel-based linear system identification using augmented Krylov subspaces
Fabio Matti, Martin Skovgaard Andersen, Tianshi Chen +1
We propose a novel Krylov subspace method for estimating the finite impulse response (FIR) of a one-dimensional linear time-invariant systems. The method approximates the system's…
QiMeng-Xpiler: Transcompiling Tensor Programs for Deep Learning Systems with a Neural-Symbolic Approach
Shouyang Dong, Yuanbo Wen, Jun Bi +10
Heterogeneous deep learning systems (DLS) such as GPUs and ASICs have been widely deployed in industrial data centers, which requires to develop multiple low-level tensor programs…
Cambricon-LLM: A Chiplet-Based Hybrid Architecture for On-Device Inference of 70B LLM
Zhongkai Yu, Shengwen Liang, Tianyun Ma +12
Deploying advanced large language models on edge devices, such as smartphones and robotics, is a growing trend that enhances user data privacy and network connectivity resilience w…
Global Adaptive Routing Algorithm Without Additional Congestion Propagation Network
Shaoli Liu, Yunji Chen, Tianshi Chen +2
Adaptive routing algorithm has been employed in multichip interconnection networks in order to improve network performance. Does a algorithm use local or global network state? This…
RepTFD: Replay Based Transient Fault Detection
Lei Li, Tianshi Chen, Yunji Chen +2
The advances in IC process make future chip multiprocessors (CMPs) more and more vulnerable to transient faults. To detect transient faults, previous core-level schemes provide red…
Numerically Efficient and Stable Algorithms for Kernel-Based Regularized System Identification Using Givens-Vector Representation
Zhuohua Shen, Junpeng Zhang, Martin S. Andersen +1
Numerically efficient and stable algorithms are essential for kernel-based regularized system identification. The state of art algorithms exploit the semiseparable structure of the…
Scaling Up Estimation of Distribution Algorithms For Continuous Optimization
Weishan Dong, Tianshi Chen, Peter Tino +1
Since Estimation of Distribution Algorithms (EDA) were proposed, many attempts have been made to improve EDAs' performance in the context of global optimization. So far, the studie…
Regularized system identification using orthonormal basis functions
Tianshi Chen, Lennart Ljung
Most of existing results on regularized system identification focus on regularized impulse response estimation. Since the impulse response model is a special case of orthonormal ba…
A Large Population Size Can Be Unhelpful in Evolutionary Algorithms
Tianshi Chen, Ke Tang, Guoliang Chen +1
The utilization of populations is one of the most important features of evolutionary algorithms (EAs). There have been many studies analyzing the impact of different population siz…
Identification of Switched Linear Systems: Persistence of Excitation and Numerical Algorithms
Biqiang Mu, Tianshi Chen, Changming Cheng +1
This paper investigates two issues on identification of switched linear systems: persistence of excitation and numerical algorithms. The main contribution is a much weaker conditio…
Novel Analysis of Population Scalability in Evolutionary Algorithms
Jun He, Tianshi Chen, Boris Mitavskiy
Population-based evolutionary algorithms (EAs) have been widely applied to solve various optimization problems. The question of how the performance of a population-based EA depends…
Scalable Anomaly Detection in Large Homogenous Populations
Henrik Ohlsson, Tianshi Chen, Sina Khoshfetrat Pakazad +2
Anomaly detection in large populations is a challenging but highly relevant problem. The problem is essentially a multi-hypothesis problem, with a hypothesis for every division of…
DLS: Directoryless Shared Last-level Cache
Daofu Liu, Yunji Chen, Qi Guo +4
Directory-based protocols have been the de facto solution for maintaining cache coherence in shared-memory parallel systems comprising multi/many cores, where each store instructio…
On kernel design for regularized LTI system identification
Tianshi Chen
There are two key issues for the kernel-based regularization method: one is how to design a suitable kernel to embed in the kernel the prior knowledge of the LTI system to be ident…
Real-Time Robust Video Object Detection System Against Physical-World Adversarial Attacks
Husheng Han, Xing Hu, Kaidi Xu +7
DNN-based video object detection (VOD) powers autonomous driving and video surveillance industries with rising importance and promising opportunities. However, adversarial patch at…
On the Asymptotic Optimality of Cross-Validation based Hyper-parameter Estimators for Regularized Least Squares Regression Problems
Biqiang Mu, Tianshi Chen, Lennart Ljung
The asymptotic optimality (a.o.) of various hyper-parameter estimators with different optimality criteria has been studied in the literature for regularized least squares regressio…
On Asymptotic Properties of Hyperparameter Estimators for Kernel-based Regularization Methods
Biqiang Mu, Tianshi Chen, Lennart Ljung
The kernel-based regularization method has two core issues: kernel design and hyperparameter estimation. In this paper, we focus on the second issue and study the properties of sev…
A Local Gaussian Process Regression Approach to Frequency Response Function Estimation
Xiaozhu Fang, Yu Xu, Tianshi Chen
Frequency response function (FRF) estimation is a classical subject in system identification. In the past two decades, there have been remarkable advances in developing local metho…
Regularized linear system identification using atomic, nuclear and kernel-based norms: the role of the stability constraint
Gianluigi Pillonetto, Tianshi Chen, Alessandro Chiuso +2
Inspired by ideas taken from the machine learning literature, new regularization techniques have been recently introduced in linear system identification. In particular, all the ad…
Pushing the Limits of Machine Design: Automated CPU Design with AI
Shuyao Cheng, Pengwei Jin, Qi Guo +16
Design activity -- constructing an artifact description satisfying given goals and constraints -- distinguishes humanity from other animals and traditional machines, and endowing m…
A Class of Convex Optimization-Based Recursive Algorithms for Identification of Stochastic Systems
Mingxia Ding, Wenxiao Zhao, Tianshi Chen
Focusing on identification, this paper develops a class of convex optimization-based criteria and correspondingly the recursive algorithms to estimate the parameter vector …
Identifiability Analysis of Noise Covariances for LTI Stochastic Systems with Unknown Inputs
He Kong, Salah Sukkarieh, Travis J. Arnold +3
Most existing works on optimal filtering of linear time-invariant (LTI) stochastic systems with arbitrary unknown inputs assume perfect knowledge of the covariances of the noises i…
BENCHIP: Benchmarking Intelligence Processors
Jinhua Tao, Zidong Du, Qi Guo +12
The increasing attention on deep learning has tremendously spurred the design of intelligence processing hardware. The variety of emerging intelligence processors requires standard…
Global Clock, Physical Time Order and Pending Period Analysis in Multiprocessor Systems
Yunji Chen, Tianshi Chen, Weiwu Hu
In multiprocessor systems, various problems are treated with Lamport's logical clock and the resultant logical time orders between operations. However, one often needs to face the…