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- Energy efficiency (8)
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- Sparse Bayesian learning (SBL) (4)
- Arrays (3)
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- Switching (3)
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Articles 1 - 30 of 115
Full-Text Articles in Engineering
Voltage Scaled Low Power Dnn Accelerator Design On Reconfigurable Platform, Rourab Paul, Sreetama Sarkar, Suman Sau, Sanghamitra Roy, Koushik Chakraborty, Amlan Chakrabarti
Voltage Scaled Low Power Dnn Accelerator Design On Reconfigurable Platform, Rourab Paul, Sreetama Sarkar, Suman Sau, Sanghamitra Roy, Koushik Chakraborty, Amlan Chakrabarti
Electrical and Computer Engineering Faculty Publications
The exponential emergence of Field-Programmable Gate Arrays (FPGAs) has accelerated research on hardware implementation of Deep Neural Networks (DNNs). Among all DNN processors, domain-specific architectures such as Google’s Tensor Processor Unit (TPU) have outperformed conventional GPUs (Graphics Processing Units) and CPUs (Central Processing Units). However, implementing low-power TPUs in reconfigurable hardware remains a challenge in this field. Voltage scaling, a popular approach for energy savings, can be challenging in FPGAs, as it may lead to timing failures if not implemented appropriately. This work presents an ultra-low-power FPGA implementation of a TPU for edge applications. We divide the systolic array of …
A Scalable Approach To Minimize Charging Costs For Electric Bus Fleets, Daniel Mortensen, Jacob Gunther
A Scalable Approach To Minimize Charging Costs For Electric Bus Fleets, Daniel Mortensen, Jacob Gunther
Electrical and Computer Engineering Faculty Publications
Incorporating battery electric buses into bus fleets faces three primary challenges: a BEB’s extended refuel time, the cost of charging, both by the consumer and the power provider, and large compute demands for planning methods. When BEBs charge, the additional demands on the grid may exceed hardware limitations, so power providers divide a consumer’s energy needs into separate meters even though doing so is expensive for both power providers and consumers. Prior work has developed a number of strategies for computing charge schedules for bus fleets; however, prior work has not worked to reduce costs by aggregating meters. Additionally, because …
Understanding Timing Error Characteristics From Overclocked Systolic Multiply–Accumulate Arrays In Fpgas, Andrew Chamberlin, Andrew Gerber, Mason Palmer, Tim Goodale, Noel Daniel Gundi, Koushik Chakraborty, Sanghamitra Roy
Understanding Timing Error Characteristics From Overclocked Systolic Multiply–Accumulate Arrays In Fpgas, Andrew Chamberlin, Andrew Gerber, Mason Palmer, Tim Goodale, Noel Daniel Gundi, Koushik Chakraborty, Sanghamitra Roy
Electrical and Computer Engineering Faculty Publications
Artificial Intelligence (AI) hardware accelerators have seen tremendous developments in recent years due to the rapid growth of AI in multiple fields. Many such accelerators comprise a Systolic Multiply–Accumulate Array (SMA) as its computational brain. In this paper, we investigate the faulty output characterization of an SMA in a real silicon FPGA board. Experiments were run on a single Zybo Z7-20 board to control for process variation at nominal voltage and in small batches to control for temperature. The FPGA is rated up to 800 MHz in the data sheet due to the max frequency of the PLL, but the …
Compressive Sensing Via Variational Bayesian Inference Under Two Widely Used Priors: Modeling, Comparison And Discussion, Mohammad Shekaramiz, Todd K. Moon
Compressive Sensing Via Variational Bayesian Inference Under Two Widely Used Priors: Modeling, Comparison And Discussion, Mohammad Shekaramiz, Todd K. Moon
Electrical and Computer Engineering Faculty Publications
Compressive sensing is a sub-Nyquist sampling technique for efficient signal acquisition and reconstruction of sparse or compressible signals. In order to account for the sparsity of the underlying signal of interest, it is common to use sparsifying priors such as Bernoulli-Gaussian-inverse Gamma (BGiG) and Gaussian-inverse Gamma (GiG) priors on the compounds of the signal. With the introduction of variational Bayesian inference, the sparse Bayesian learning (SBL) methods for solving the inverse problem of compressive sensing have received significant interest as the SBL methods become more efficient in terms of execution time. In this paper, we consider the sparse signal recovery …
Uptpu: Improving Energy Efficiency Of A Tensor Processing Unit Through Underutilization Based Power-Gating, Pramesh Pandey, Noel Daniel Gundi, Koushik Chakraborty, Sanghamitra Roy
Uptpu: Improving Energy Efficiency Of A Tensor Processing Unit Through Underutilization Based Power-Gating, Pramesh Pandey, Noel Daniel Gundi, Koushik Chakraborty, Sanghamitra Roy
Electrical and Computer Engineering Faculty Publications
The AI boom is bringing a plethora of domain-specific architectures for Neural Network computations. Google's Tensor Processing Unit (TPU), a Deep Neural Network (DNN) accelerator, has replaced the CPUs/GPUs in its data centers, claiming more than 15 × rate of inference. However, the unprecedented growth in DNN workloads with the widespread use of AI services projects an increasing energy consumption of TPU based data centers. In this work, we parametrize the extreme hardware underutilization in TPU systolic array and propose UPTPU: an intelligent, dataflow adaptive power-gating paradigm to provide a staggering 3.5 ×-6.5× energy efficiency to TPU for different input …
On Correctness, Precision, And Performance In Quantitative Verification: Qcomp 2020 Competition Report, Carlos E. Budde, Arnd Hartmanns, Michaela Klauck, Jan Křetínský, David Parker, Tim Quatmann, Andrea Turrini, Zhen Zhang
On Correctness, Precision, And Performance In Quantitative Verification: Qcomp 2020 Competition Report, Carlos E. Budde, Arnd Hartmanns, Michaela Klauck, Jan Křetínský, David Parker, Tim Quatmann, Andrea Turrini, Zhen Zhang
Electrical and Computer Engineering Faculty Publications
Quantitative verification tools compute probabilities, expected rewards, or steady-state values for formal models of stochastic and timed systems. Exact results often cannot be obtained efficiently, so most tools use floating-point arithmetic in iterative algorithms that approximate the quantity of interest. Correctness is thus defined by the desired precision and determines performance. In this paper, we report on the experimental evaluation of these trade-offs performed in QComp 2020: the second friendly competition of tools for the analysis of quantitative formal models. We survey the precision guarantees - ranging from exact rational results to statistical confidence statements - offered by the nine …
Challenges And Opportunities In Near-Threshold Dnn Accelerators Around Timing Errors, Pramesh Pandey, Noel Daniel Gundi, Prabal Basu, Tahmoures Shabanian, Mitchell Craig Patrick, Koushik Chakraborty, Sanghamitra Roy
Challenges And Opportunities In Near-Threshold Dnn Accelerators Around Timing Errors, Pramesh Pandey, Noel Daniel Gundi, Prabal Basu, Tahmoures Shabanian, Mitchell Craig Patrick, Koushik Chakraborty, Sanghamitra Roy
Electrical and Computer Engineering Faculty Publications
AI evolution is accelerating and Deep Neural Network (DNN) inference accelerators are at the forefront of ad hoc architectures that are evolving to support the immense throughput required for AI computation. However, much more energy efficient design paradigms are inevitable to realize the complete potential of AI evolution and curtail energy consumption. The Near-Threshold Computing (NTC) design paradigm can serve as the best candidate for providing the required energy efficiency. However, NTC operation is plagued with ample performance and reliability concerns arising from the timing errors. In this paper, we dive deep into DNN architecture to uncover some unique challenges …
Titan: Uncovering The Paradigm Shift In Security Vulnerability At Near-Threshold Computing, Prabal Basu, Pramesh Pandey, Aatreyi Bal, Chidhambaranathan Rajamanikkam, Koushik Chakraborty, Sanghamitra Roy
Titan: Uncovering The Paradigm Shift In Security Vulnerability At Near-Threshold Computing, Prabal Basu, Pramesh Pandey, Aatreyi Bal, Chidhambaranathan Rajamanikkam, Koushik Chakraborty, Sanghamitra Roy
Electrical and Computer Engineering Faculty Publications
In this paper, we investigate the emerging security threats at Near-Threshold Computing (NTC) that are poised to jeopardize the trustworthy operation of future low-power electronic devices. A substantial research effort over the last decade has bolstered energy efficient operation in low-power computing. However, innovation in low-power security has received only marginal attention, thwarting a ubiquitous adoption of critical Internet of Things applications, such as wearable gadgets. Using a cross-layer methodology, we demonstrate that the timing fault vulnerability of a circuit rapidly increases as the operating conditions of the transistor devices shift from super-threshold to near-threshold values. Exploiting this vulnerability, we …
Greentpu: Predictive Design Paradigm For Improving Timing Error Resilience Of A Near-Threshold Tensor Processing Unit, Pramesh Pandey, Prabal Basu, Koushik Chakraborty, Sanghamitra Roy
Greentpu: Predictive Design Paradigm For Improving Timing Error Resilience Of A Near-Threshold Tensor Processing Unit, Pramesh Pandey, Prabal Basu, Koushik Chakraborty, Sanghamitra Roy
Electrical and Computer Engineering Faculty Publications
The emergence of hardware accelerators has brought about several orders of magnitude improvement in the speed of the deep neural-network (DNN) inference. Among such DNN accelerators, the Google tensor processing unit (TPU) has transpired to be the best-in-class, offering more than 15\times speedup over the contemporary GPUs. However, the rapid growth in several DNN workloads conspires to escalate the energy consumptions of the TPU-based data-centers. In order to restrict the energy consumption of TPUs, we propose GreenTPU - a low-power near-threshold (NTC) TPU design paradigm. To ensure a high inference accuracy at a low-voltage operation, GreenTPU identifies the patterns in …
The Gridded Retarding Ion Drift Sensor For The Petitsat Cubesat Mission, Ryan L. Davidson, B. Oborn, E. F. Robertson, S. Noel, G. D. Earle, J. Green, J. Kramer
The Gridded Retarding Ion Drift Sensor For The Petitsat Cubesat Mission, Ryan L. Davidson, B. Oborn, E. F. Robertson, S. Noel, G. D. Earle, J. Green, J. Kramer
Electrical and Computer Engineering Faculty Publications
The Gridded Retarding Ion Drift Sensor (GRIDS) is a small sensor that will fly on the 6 U petitSat CubeSat. It is designed to measure the three-dimensional plasma drift velocity vector in the Earth’s ionosphere. The GRIDS also supplies information about the ion temperature, ion density, and the ratio of light to heavy ions present in the ionospheric plasma. It utilizes well-proven techniques that have been successfully validated by similar instruments on larger satellite missions while meeting CubeSat-compatible requirements for low mass, size, and power consumption. GRIDS performs the functions of a Retarding Potential Analyzer (RPA) and an Ion Drift …
Estimation Of Autoregressive Parameters From Noisy Observations Using Iterated Covariance Updates, Todd K. Moon, Jacob H. Gunther
Estimation Of Autoregressive Parameters From Noisy Observations Using Iterated Covariance Updates, Todd K. Moon, Jacob H. Gunther
Electrical and Computer Engineering Faculty Publications
Estimating the parameters of the autoregressive (AR) random process is a problem that has been well-studied. In many applications, only noisy measurements of AR process are available. The effect of the additive noise is that the system can be modeled as an AR model with colored noise, even when the measurement noise is white, where the correlation matrix depends on the AR parameters. Because of the correlation, it is expedient to compute using multiple stacked observations. Performing a weighted least-squares estimation of the AR parameters using an inverse covariance weighting can provide significantly better parameter estimates, with improvement increasing with …
Reducing Road Wear While Ensuring Comfort And Charging Constraints For Dynamically Charged Passenger Vehicles Through Noise-Shaped Path Variations, Clint Jay Ferrin, Randall Christensen
Reducing Road Wear While Ensuring Comfort And Charging Constraints For Dynamically Charged Passenger Vehicles Through Noise-Shaped Path Variations, Clint Jay Ferrin, Randall Christensen
Electrical and Computer Engineering Faculty Publications
Dynamically charged vehicles suffer from power loss during wireless power transfer due to receiver and transmitter coil misalignment while driving. Autonomous, dynamically charged vehicles can maximize wireless power transfer by minimizing the misalignment, but the repeated high-precision driving increases road wear. To avoid unnecessary road wear and rutting, a noise shaping filter is proposed that adds variability to a vehicle's trajectory that complies with passenger acceleration and position constraints. However, introducing variability into an optimal charging path also risks depleting battery life prior to destination arrival. Therefore, a path planner is proposed that guarantees average charge within a specified probability …
Effort: Enhancing Energy Efficiency And Error Resilience Of A Near-Threshold Tensor Processing Unit, Noel Daniel Gundi, Tahmoures Shabanian, Prabal Basu, Pramesh Pandey, Sanghamitra Roy, Koushik Chakraborty, Zhen Zhang
Effort: Enhancing Energy Efficiency And Error Resilience Of A Near-Threshold Tensor Processing Unit, Noel Daniel Gundi, Tahmoures Shabanian, Prabal Basu, Pramesh Pandey, Sanghamitra Roy, Koushik Chakraborty, Zhen Zhang
Electrical and Computer Engineering Faculty Publications
Modern deep neural network (DNN) applications demand a remarkable processing throughput usually unmet by traditional Von Neumann architectures. Consequently, hardware accelerators, comprising a sea of multiplier and accumulate (MAC) units, have recently gained prominence in accelerating DNN inference engine. For example, Tensor Processing Units (TPU) account for a lion's share of Google's datacenter inference operations. The proliferation of real-time DNN predictions is accompanied with a tremendous energy budget. In quest of trimming the energy footprint of DNN accelerators, we propose EFFORT-an energy optimized, yet high performance TPU architecture, operating at the Near-Threshold Computing (NTC) region. EFFORT promotes a better-than-worst-case design …
A Practitioner’S Guide To Small Unmanned Aerial Systems For Bridge Inspection, Sattar Dorafshan, Robert J. Thomas, Calvin Coopmans, Marc Maguire
A Practitioner’S Guide To Small Unmanned Aerial Systems For Bridge Inspection, Sattar Dorafshan, Robert J. Thomas, Calvin Coopmans, Marc Maguire
Electrical and Computer Engineering Faculty Publications
Small unmanned aerial system(s) (sUAS) are rapidly emerging as a practical means of performing bridge inspections. Under the right condition, sUAS assisted inspections can be safer, faster, and less costly than manned inspections. Many Departments of Transportation in the United States are in the early stages of adopting this emerging technology. However, definitive guidelines for the selection of equipment for various types of bridge inspections or for the possible challenges during sUAS assisted inspections are absent. Given the large investments of time and capital associated with deploying a sUAS assisted bridge inspection program, a synthesis of authors experiences will be …
Stamina: Stochastic Approximate Model-Checker For Infinite-State Analysis, Thackur Neupane, Chris J. Myers, Curtis Madsen, Hao Zheng, Zhen Zhang
Stamina: Stochastic Approximate Model-Checker For Infinite-State Analysis, Thackur Neupane, Chris J. Myers, Curtis Madsen, Hao Zheng, Zhen Zhang
Electrical and Computer Engineering Faculty Publications
Stochastic model checking is a technique for analyzing systems that possess probabilistic characteristics. However, its scalability is limited as probabilistic models of real-world applications typically have very large or infinite state space. This paper presents a new infinite state CTMC model checker, STAMINA, with improved scalability. It uses a novel state space approximation method to reduce large and possibly infinite state CTMC models to finite state representations that are amenable to existing stochastic model checkers. It is integrated with a new property-guided state expansion approach that improves the analysis accuracy. Demonstration of the tool on several benchmark examples shows promising …
Approximation Techniques For Stochastic Analysis Of Biological Systems, Thakur Neupane, Zhen Zhang, Curtis Madsen, Hao Zheng, Chris J. Myers
Approximation Techniques For Stochastic Analysis Of Biological Systems, Thakur Neupane, Zhen Zhang, Curtis Madsen, Hao Zheng, Chris J. Myers
Electrical and Computer Engineering Faculty Publications
There has been an increasing demand for formal methods in the design process of safety-critical synthetic genetic circuits. Probabilistic model checking techniques have demonstrated significant potential in analyzing the intrinsic probabilistic behaviors of complex genetic circuit designs. However, its inability to scale limits its applicability in practice. This chapter addresses the scalability problem by presenting a state-space approximation method to remove unlikely states resulting in a reduced, finite state representation of the infinite-state continuous-time Markov chain that is amenable to probabilistic model checking. The proposed method is evaluated on a design of a genetic toggle switch. Comparisons with another state-of-the-art …
Exploration Vs. Data Refinement Via Multiple Mobile Sensors, Mohammad Shekaramiz, Todd K. Moon, Jacob H. Gunther
Exploration Vs. Data Refinement Via Multiple Mobile Sensors, Mohammad Shekaramiz, Todd K. Moon, Jacob H. Gunther
Electrical and Computer Engineering Faculty Publications
We examine the deployment of multiple mobile sensors to explore an unknown region to map regions containing concentration of a physical quantity such as heat, electron density, and so on. The exploration trades off between two desiderata: to continue taking data in a region known to contain the quantity of interest with the intent of refining the measurements vs. taking data in unobserved areas to attempt to discover new regions where the quantity may exist. Making reasonable and practical decisions to simultaneously fulfill both goals of exploration and data refinement seem to be hard and contradictory. For this purpose, we …
Energy Efficient Network-On-Chip Architectures For Many-Core Near-Threshold Computing System, Chidhambaranathan Rajamanikkam, Jayashankara S. Rajesh, Koushik Chakraborty, Meher Samineni
Energy Efficient Network-On-Chip Architectures For Many-Core Near-Threshold Computing System, Chidhambaranathan Rajamanikkam, Jayashankara S. Rajesh, Koushik Chakraborty, Meher Samineni
Electrical and Computer Engineering Faculty Publications
Near threshold computing has unraveled a promising design space for energy efficient computing. However, it is still plagued by sub-optimal system performance. Application characteristics and hardware non-idealities of conventional architectures (those optimized for nominal voltage) prevent us from fully leveraging the potential of NTC systems. Increasing the computational core count still forms the bedrock of a multitude of contemporary works that address the problem of performance degradation in NTC systems. However, these works do not categorically address the shortcomings of the conventional on-chip interconnect fabric in a many core environment. In this work, we quantitatively demonstrate the performance bottleneck created …
Details On Csa-Sbl: An Algorithm For Sparse Bayesian Learning Boosted By Partial Erroneous Support Knowledge, Mohammad Shekaramiz, Todd K. Moon, Jacob H. Gunther
Details On Csa-Sbl: An Algorithm For Sparse Bayesian Learning Boosted By Partial Erroneous Support Knowledge, Mohammad Shekaramiz, Todd K. Moon, Jacob H. Gunther
Electrical and Computer Engineering Faculty Publications
This report provides details on CSA-SBL(VB) algorithm for the recovery of sparse signals with unknown clustering pattern. More specifically, we deal with the recovery of sparse signals with unknown clustering pattern in the case of having partial erroneous prior knowledge on the supports of the signal. In [1], we provided a modified sparse Bayesian learning model to incorporate prior knowledge and simultaneously learn the unknown clustering pattern. For this purpose, we added one more layer to support-aided sparse Bayesian learning algorithm (SA-SBL) that was proposed in [2]. This layer adds a prior on the shape parameters of Gamma distributions, those …
Bayesian Compressive Sensing Of Sparse Signals With Unknown Clustering Patterns, Mohammad Shekaramiz, Todd K. Moon, Jacob H. Gunther
Bayesian Compressive Sensing Of Sparse Signals With Unknown Clustering Patterns, Mohammad Shekaramiz, Todd K. Moon, Jacob H. Gunther
Electrical and Computer Engineering Faculty Publications
We consider the sparse recovery problem of signals with an unknown clustering pattern in the context of multiple measurement vectors (MMVs) using the compressive sensing (CS) technique. For many MMVs in practice, the solution matrix exhibits some sort of clustered sparsity pattern, or clumpy behavior, along each column, as well as joint sparsity across the columns. In this paper, we propose a new sparse Bayesian learning (SBL) method that incorporates a total variation-like prior as a measure of the overall clustering pattern in the solution. We further incorporate a parameter in this prior to account for the emphasis on the …
Details On O-Sbl(Mcmc): A Compressive Sensing Algorithm For Sparse Signal Recovery For The Smv/Mmv Problem Using Sparse Bayesian Learning And Markov Chain Monte Carlo Inference, Mohammad Shekaramiz, Todd K. Moon, Jacob H. Gunther
Details On O-Sbl(Mcmc): A Compressive Sensing Algorithm For Sparse Signal Recovery For The Smv/Mmv Problem Using Sparse Bayesian Learning And Markov Chain Monte Carlo Inference, Mohammad Shekaramiz, Todd K. Moon, Jacob H. Gunther
Electrical and Computer Engineering Faculty Publications
This report provides details on O-SBL(MCMC) algorithm for the recovery of jointly-sparse signals for the multiple measurement vector (MMV) problem. For the MMVs with this structure, the solution matrix, which is a collection of sparse vectors, is expected to exhibit joint sparsity across the columns. The notion of joint sparsity here means that the columns of the solution matrix share common supports. This algorithm employs a sparse Bayesian learning (SBL) model to encourage the joint sparsity structure across the columns of the solution. While the proposed algorithm is constructed for the MMV problems, it can also be applied to the …
A Note On Bayesian Linear Regression, Mohammad Shekaramiz, Todd K. Moon
A Note On Bayesian Linear Regression, Mohammad Shekaramiz, Todd K. Moon
Electrical and Computer Engineering Faculty Publications
In this report, we briefly discuss Bayesian linear regression as well as the proof for the inference to perform prediction based on the training data using this technique.
Details On Amp-B-Sbl: An Algorithm For Recovery Of Clustered Sparse Signals Using Approximate Message Passing [1-3], Mohammad Shekaramiz, Todd K. Moon, Jacob H. Gunther
Details On Amp-B-Sbl: An Algorithm For Recovery Of Clustered Sparse Signals Using Approximate Message Passing [1-3], Mohammad Shekaramiz, Todd K. Moon, Jacob H. Gunther
Electrical and Computer Engineering Faculty Publications
Solving the inverse problem of compressive sensing in the context of single measurement vector (SMV) problem with an unknown block-sparsity structure is considered. For this purpose, we propose a sparse Bayesian learning (SBL) algorithm simplified via the approximate message passing (AMP) framework. In order to encourage the block-sparsity structure, we incorporate the concept of total variation, called Sigma-Delta, as a measure of block-sparsity on the support set of the solution. The AMP framework reduces the computational load of the proposed SBL algorithm and as a result makes it faster compared to the message passing framework. Furthermore, in terms of the …
A Note On Kriging And Gaussian Processes, Mohammad Shekaramiz, Todd K. Moon, Jacob H. Gunther
A Note On Kriging And Gaussian Processes, Mohammad Shekaramiz, Todd K. Moon, Jacob H. Gunther
Electrical and Computer Engineering Faculty Publications
An introduction to gaussian processes and kriging.
Details On Gaussian Process Regression (Gpr) And Semi-Gpr Modeling, Mohammad Shekaramiz, Todd K. Moon, Jacob H. Gunther
Details On Gaussian Process Regression (Gpr) And Semi-Gpr Modeling, Mohammad Shekaramiz, Todd K. Moon, Jacob H. Gunther
Electrical and Computer Engineering Faculty Publications
This report tends to provide details on how to perform predictions using Gaussian process regression (GPR) modeling. In this case, we represent proofs for prediction using non-parametric GPR modeling for noise-free predictions as well as prediction using semi-parametric GPR for noisy observations.
On The Stability Analysis Of Perturbed Continuous T-S Fuzzy Models, Mohammad Shekaramiz, Farid Sheikholeslam
On The Stability Analysis Of Perturbed Continuous T-S Fuzzy Models, Mohammad Shekaramiz, Farid Sheikholeslam
Electrical and Computer Engineering Faculty Publications
This paper deals with the stability problem of continuous-time Takagi-Sugeno (T-S) fuzzy models. Based on the Tanaka and Sugeno theorem, a new systematic method is introduced to investigate the asymptotic stability of T-S models in case of having second-order and symmetric state matrices. This stability criterion has the merit that selection of the common positive-definite matrix P is independent of the sub-diagonal entries of the state matrices. It means for a set of fuzzy models having the same main diagonal state matrices, it suffices to apply the method once. Furthermore, the method can be applied to T-S models having certain …
On The Stability Analysis Of Linear Continuous-Time Distributed Systems, Mohammad Shekaramiz
On The Stability Analysis Of Linear Continuous-Time Distributed Systems, Mohammad Shekaramiz
Electrical and Computer Engineering Faculty Publications
This paper discusses the stability problem of linear continuous-time distributed systems. When dealing with large-scale systems, usually there is not thorough knowledge of the interconnection models between different parts of the entire system. In this case, a useful stability analysis method should be able to deal with high dimensional systems accompanied with bounded uncertainties for its interconnections. In this paper, in order to formulate the stability criterion for large-scale systems, stability analysis of LTI systems is first considered. Based on the existing methods for estimating the spectra of square matrices, sufficient criteria are proposed to guarantee the asymptotic stability of …
Simple Stability Criteria For Uncertain Continuous-Time Linear Systems, Mohammad Shekaramiz
Simple Stability Criteria For Uncertain Continuous-Time Linear Systems, Mohammad Shekaramiz
Electrical and Computer Engineering Faculty Publications
This paper mainly deals with the stability problem of continuous-time linear systems having uncertainties. Instead of using the tradition types of Lyapunov functions, this paper provides a very different method to investigate the stability of such systems. Hence, it reduces the conservativeness of having structured uncertainties belonging to convex sets. Based on a famous theorem that specifies regions containing all the eigenvalues of a complex square matrix, sufficient criteria are proposed to guarantee the asymptotic stability of linear systems. The main merit of this method is in analyzing linear systems having uncertainties. Moreover, the proposed criteria can also be used …
The Internet Of Energy: Architectures, Cyber Security, And Applications, Kun Wang, Yan Zhang, Song Guo, Mianxiong Dong, Rose Qingyang Hu, Lei He
The Internet Of Energy: Architectures, Cyber Security, And Applications, Kun Wang, Yan Zhang, Song Guo, Mianxiong Dong, Rose Qingyang Hu, Lei He
Electrical and Computer Engineering Faculty Publications
The energy crisis and carbon emissions have become two critical concerns globally. As a very promising solution, the concept of Internet of Energy has appeared to tackle these challenges. The Internet of Energy is a new power generation paradigm developing a revolutionary vision of smart grids into the Internet. The communication infrastructure is an essential component for implementing the Internet of Energy. A scalable and robust communication infrastructure is crucial in both operating and maintaining smart energy systems. The wide-scale implementation and development of Internet of Energy into industrial applications should take into account the following challenges:
Trident: Comprehensive Choke Error Mitigation In Ntc Systems, Aatreyi Bal, Sanghamitra Roy, Koushik Chakraborty
Trident: Comprehensive Choke Error Mitigation In Ntc Systems, Aatreyi Bal, Sanghamitra Roy, Koushik Chakraborty
Electrical and Computer Engineering Faculty Publications
Near threshold computing (NTC) systems have been inherently plagued with heightened process variation (PV) sensitivity. Choke points are an intriguing manifestation of this PV sensitivity. In this paper, we explore the probability of minimum timing violations, caused by choke points, in an NTC system, and their nontrivial impacts on the system reliability. We show that conventional timing error mitigation techniques are inefficient in tackling choke point-induced minimum timing violations. Consequently, we propose a comprehensive error mitigation technique, Trident, to tackle choke points at NTC. Trident offers a 1.37 × performance improvement and a 1.11 × energy-efficiency gain over Razor at …