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Articles 1 - 30 of 652
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 …
Exponential Fusion Of Interpolated Frames Network (Efif-Net): Advancing Multi-Frame Image Super-Resolution With Convolutional Neural Networks, Hamed Elwarfalli, Dylan Flaute, Russell C. Hardie
Exponential Fusion Of Interpolated Frames Network (Efif-Net): Advancing Multi-Frame Image Super-Resolution With Convolutional Neural Networks, Hamed Elwarfalli, Dylan Flaute, Russell C. Hardie
Electrical and Computer Engineering Faculty Publications
Convolutional neural networks (CNNs) have become instrumental in advancing multi-frame image super-resolution (SR), a technique that merges multiple low-resolution images of the same scene into a high-resolution image. In this paper, a novel deep learning multi-frame SR algorithm is introduced. The proposed CNN model, named Exponential Fusion of Interpolated Frames Network (EFIF-Net), seamlessly integrates fusion and restoration within an end-to-end network. Key features of the new EFIF-Net include a custom exponentially weighted fusion (EWF) layer for image fusion and a modification of the Residual Channel Attention Network for restoration to deblur the fused image. Input frames are registered with subpixel …
Intelligent Millimeter-Wave System For Human Activity Monitoring For Telemedicine, Abdullah K. Alhazmi, Mubarak A. Alanazi, Awwad H. Alshehry, Saleh M. Alshahry, Jennifer Jaszek, Cameron Djukic, Anna Brown, Kurt Jackson, Vamsy P. Chodavarapu
Intelligent Millimeter-Wave System For Human Activity Monitoring For Telemedicine, Abdullah K. Alhazmi, Mubarak A. Alanazi, Awwad H. Alshehry, Saleh M. Alshahry, Jennifer Jaszek, Cameron Djukic, Anna Brown, Kurt Jackson, Vamsy P. Chodavarapu
Electrical and Computer Engineering Faculty Publications
Telemedicine has the potential to improve access and delivery of healthcare to diverse and aging populations. Recent advances in technology allow for remote monitoring of physiological measures such as heart rate, oxygen saturation, blood glucose, and blood pressure. However, the ability to accurately detect falls and monitor physical activity remotely without invading privacy or remembering to wear a costly device remains an ongoing concern. Our proposed system utilizes a millimeter-wave (mmwave) radar sensor (IWR6843ISK-ODS) connected to an NVIDIA Jetson Nano board for continuous monitoring of human activity. We developed a PointNet neural network for real-time human activity monitoring that can …
Generalized Conditional Feedback System With Model Uncertainty, Chengbo Dai, Zhiqiang Gao, Yangquan Chen, Donghai Li
Generalized Conditional Feedback System With Model Uncertainty, Chengbo Dai, Zhiqiang Gao, Yangquan Chen, Donghai Li
Electrical and Computer Engineering Faculty Publications
Model uncertainty creates a largely open challenge for industrial process control, which causes a trade-off between robustness and performance optimality. In such a case, we propose a generalized conditional feedback (GCF) system to largely eliminate conflicts between robustness and performance optimality. This approach leverages a nominal model to design an optimal control in the virtual domain and defines an ancillary feedback controller to drive the physical process to track the trajectory of the virtual domain. The effectiveness of the proposed GCF scheme is demonstrated in a simulation for six typical industrial processes and three model-based control methods, and in a …
Blockchain-Based Applications For Smart Grids: An Umbrella Review, Wenbing Zhao, Quan Qi, Jiong Zhou, Xiong Luo Qi
Blockchain-Based Applications For Smart Grids: An Umbrella Review, Wenbing Zhao, Quan Qi, Jiong Zhou, Xiong Luo Qi
Electrical and Computer Engineering Faculty Publications
This article presents an umbrella review of blockchain-based smart grid applications. By umbrella review, we mean that our review is based on systematic reviews of this topic. We aim to synthesize the findings from these systematic reviews and gain deeper insights into this discipline. After studying the systematic reviews, we find it imperative to provide a concise and authoritative description of blockchain technology because many technical inaccuracies permeate many of these papers. This umbrella review is guided by five research questions. The first research question concerns the types of blockchain-based smart grid applications. Existing systematic reviews rarely used a systematic …
Performance Modeling And Optimization For A Fog-Based Iot Platform, Shensheng Tang
Performance Modeling And Optimization For A Fog-Based Iot Platform, Shensheng Tang
Electrical and Computer Engineering Faculty Publications
A fog-based IoT platform model involving three layers, i.e., IoT devices, fog nodes, and the cloud, was proposed using an open Jackson network with feedback. The system performance was analyzed for individual subsystems, and the overall system was based on different input parameters. Interesting performance metrics were derived from analytical results. A resource optimization problem was developed and solved to determine the optimal service rates at individual fog nodes under some constraint conditions. Numerical evaluations for the performance and the optimization problem are provided for further understanding of the analysis. The modeling and analysis, as well as the optimization design …
Towards An Evolved Immersive Experience: Exploring 5g-And Beyond-Enabled Ultra-Low-Latency Communications For Augmented And Virtual Reality, Ananya Hazarika, Mehdi Rahmati
Towards An Evolved Immersive Experience: Exploring 5g-And Beyond-Enabled Ultra-Low-Latency Communications For Augmented And Virtual Reality, Ananya Hazarika, Mehdi Rahmati
Electrical and Computer Engineering Faculty Publications
Augmented reality and virtual reality technologies are witnessing an evolutionary change in the 5G and Beyond (5GB) network due to their promising ability to enable an immersive and interactive environment by coupling the virtual world with the real one. However, the requirement of low-latency connectivity, which is defined as the end-to-end delay between the action and the reaction, is very crucial to leverage these technologies for a high-quality immersive experience. This paper provides a comprehensive survey and detailed insight into various advantageous approaches from the hardware and software perspectives, as well as the integration of 5G technology, towards 5GB, in …
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 …
Effective Short Text Classification Via The Fusion Of Hybrid Features For Iot Social Data, Xiong Luo, Zhijian Yu, Zhigang Zhao, Wenbing Zhao, Jenq-Haur Wang
Effective Short Text Classification Via The Fusion Of Hybrid Features For Iot Social Data, Xiong Luo, Zhijian Yu, Zhigang Zhao, Wenbing Zhao, Jenq-Haur Wang
Electrical and Computer Engineering Faculty Publications
Nowadays short texts can be widely found in various social data in relation to the 5G-enabled Internet of Things (IoT). Short text classification is a challenging task due to its sparsity and the lack of context. Previous studies mainly tackle these problems by enhancing the semantic information or the statistical information individually. However, the improvement achieved by a single type of information is limited, while fusing various information may help to improve the classification accuracy more effectively. To fuse various information for short text classification, this article proposes a feature fusion method that integrates the statistical feature and the comprehensive …
A Patient-Specific Algorithm For Lung Segmentation In Chest Radiographs, Manawaduge Supun De Silva, Barath Narayanan Narayanan, Russell C. Hardie
A Patient-Specific Algorithm For Lung Segmentation In Chest Radiographs, Manawaduge Supun De Silva, Barath Narayanan Narayanan, Russell C. Hardie
Electrical and Computer Engineering Faculty Publications
Lung segmentation plays an important role in computer-aided detection and diagnosis using chest radiographs (CRs). Currently, the U-Net and DeepLabv3+ convolutional neural network architectures are widely used to perform CR lung segmentation. To boost performance, ensemble methods are often used, whereby probability map outputs from several networks operating on the same input image are averaged. However, not all networks perform adequately for any specific patient image, even if the average network performance is good. To address this, we present a novel multi-network ensemble method that employs a selector network. The selector network evaluates the segmentation outputs from several networks; on …
Glaciernet2: A Hybrid Multi-Model Learning Architecture For Alpine Glacier Mapping, Zhiyuan Xie, Umesh K. Haritashya, Vijayan K. Asari, Michael P. Bishop, Jeffrey S. Kargel, Theus Aspiras
Glaciernet2: A Hybrid Multi-Model Learning Architecture For Alpine Glacier Mapping, Zhiyuan Xie, Umesh K. Haritashya, Vijayan K. Asari, Michael P. Bishop, Jeffrey S. Kargel, Theus Aspiras
Electrical and Computer Engineering Faculty Publications
In recent decades, climate change has significantly affected glacier dynamics, resulting in mass loss and an increased risk of glacier-related hazards including supraglacial and proglacial lake development, as well as catastrophic outburst flooding. Rapidly changing conditions dictate the need for continuous and detailed ob-servations and analysis of climate-glacier dynamics. Thematic and quantitative information regarding glacier geometry is fundamental for understanding climate forcing and the sensitivity of glaciers to climate change, however, accurately mapping debris-cover glaciers (DCGs) is notoriously difficult based upon the use of spectral information and conventional machine-learning techniques. The objective of this research is to improve upon an …
Towards A Low-Cost Solution For Gait Analysis Using Millimeter Wave Sensor And Machine Learning, Mubarak A. Alanazi, Abdullah K. Alhazmi, Osama Alsattam, Kara Gnau, Meghan Brown, Shannon Thiel, Kurt Jackson, Vamsy P. Chodavarapu
Towards A Low-Cost Solution For Gait Analysis Using Millimeter Wave Sensor And Machine Learning, Mubarak A. Alanazi, Abdullah K. Alhazmi, Osama Alsattam, Kara Gnau, Meghan Brown, Shannon Thiel, Kurt Jackson, Vamsy P. Chodavarapu
Electrical and Computer Engineering Faculty Publications
Human Activity Recognition (HAR) that includes gait analysis may be useful for various rehabilitation and telemonitoring applications. Current gait analysis methods, such as wearables or cameras, have privacy and operational constraints, especially when used with older adults. Millimeter-Wave (MMW) radar is a promising solution for gait applications because of its low-cost, better privacy, and resilience to ambient light and climate conditions. This paper presents a novel human gait analysis method that combines the micro-Doppler spectrogram and skeletal pose estimation using MMW radar for HAR. In our approach, we used the Texas Instruments IWR6843ISK-ODS MMW radar to obtain the micro-Doppler spectrogram …
Imnets: Deep Learning Using An Incremental Modular Network Synthesis Approach For Medical Imaging Applications, Redha A. Ali, Russell C. Hardie, Barath Narayanan Narayanan, Temesguen Messay
Imnets: Deep Learning Using An Incremental Modular Network Synthesis Approach For Medical Imaging Applications, Redha A. Ali, Russell C. Hardie, Barath Narayanan Narayanan, Temesguen Messay
Electrical and Computer Engineering Faculty Publications
Deep learning approaches play a crucial role in computer-aided diagnosis systems to support clinical decision-making. However, developing such automated solutions is challenging due to the limited availability of annotated medical data. In this study, we proposed a novel and computationally efficient deep learning approach to leverage small data for learning generalizable and domain invariant representations in different medical imaging applications such as malaria, diabetic retinopathy, and tuberculosis. We refer to our approach as Incremental Modular Network Synthesis (IMNS), and the resulting CNNs as Incremental Modular Networks (IMNets). Our IMNS approach is to use small network modules that we call SubNets …
Machine Learning Used In Biomedical Computing And Intelligence Healthcare, Volume Ii, Honghao Gao, Ying Li, Zijian Zhang, Wenbing Zhao
Machine Learning Used In Biomedical Computing And Intelligence Healthcare, Volume Ii, Honghao Gao, Ying Li, Zijian Zhang, Wenbing Zhao
Electrical and Computer Engineering Faculty Publications
No abstract provided.
Microscopic Nuclei Classification, Segmentation, And Detection With Improved Deep Convolutional Neural Networks (Dcnn), Md Zahangir Alom, Vijayan K. Asari, Anil Parwani, Tarek M. Taha
Microscopic Nuclei Classification, Segmentation, And Detection With Improved Deep Convolutional Neural Networks (Dcnn), Md Zahangir Alom, Vijayan K. Asari, Anil Parwani, Tarek M. Taha
Electrical and Computer Engineering Faculty Publications
Background Nuclei classification, segmentation, and detection from pathological images are challenging tasks due to cellular heterogeneity in the Whole Slide Images (WSI). Methods In this work, we propose advanced DCNN models for nuclei classification, segmentation, and detection tasks. The Densely Connected Neural Network (DCNN) and Densely Connected Recurrent Convolutional Network (DCRN) models are applied for the nuclei classification tasks. The Recurrent Residual U-Net (R2U-Net) and the R2UNet-based regression model named the University of Dayton Net (UD-Net) are applied for nuclei segmentation and detection tasks respectively. The experiments are conducted on publicly available datasets, including Routine Colon Cancer (RCC) classification and …
Towards Improved Inertial Navigation By Reducing Errors Using Deep Learning Methodology, Hua Chen, Tarek M. Taha, Vamsy P. Chodavarapu
Towards Improved Inertial Navigation By Reducing Errors Using Deep Learning Methodology, Hua Chen, Tarek M. Taha, Vamsy P. Chodavarapu
Electrical and Computer Engineering Faculty Publications
Autonomous vehicles make use of an Inertial Navigation System (INS) as part of vehicular sensor fusion in many situations including GPS-denied environments such as dense urban places, multi-level parking structures, and areas with thick tree-coverage. The INS unit incorporates an Inertial Measurement Unit (IMU) to process the linear acceleration and angular velocity data to obtain orientation, position, and velocity information using mechanization equations. In this work, we describe a novel deep-learning-based methodology, using Convolutional Neural Networks (CNN), to reduce errors from MEMS IMU sensors. We develop a CNN-based approach that can learn from the responses of a particular inertial sensor …
Recent Advances Of Wind-Solar Hybrid Renewable Energy Systems For Power Generation: A Review, Pranoy Roy, Jiangbiao He, Tiefu Zhao, Yash Veer Singh
Recent Advances Of Wind-Solar Hybrid Renewable Energy Systems For Power Generation: A Review, Pranoy Roy, Jiangbiao He, Tiefu Zhao, Yash Veer Singh
Electrical and Computer Engineering Faculty Publications
A hybrid renewable energy source (HRES) consists of two or more renewable energy sources, such as wind turbines and photovoltaic systems, utilized together to provide increased system efficiency and improved stability in energy supply to a certain degree. The objective of this study is to present a comprehensive review of wind-solar HRES from the perspectives of power architectures, mathematical modeling, power electronic converter topologies, and design optimization algorithms. Since the uncertainty of HRES can be reduced further by including an energy storage system, this paper presents several hybrid energy storage system coupling technologies, highlighting their major advantages and disadvantages. Various …
Application Of Deep Neural Networks To Distribution System State Estimation And Forecasting, James P. Carmichael, Yuan Liao
Application Of Deep Neural Networks To Distribution System State Estimation And Forecasting, James P. Carmichael, Yuan Liao
Electrical and Computer Engineering Faculty Publications
Classical neural networks such as feedforward multi-layer perceptron models (MLPs) are well established as universal approximators and as such, show promise in applications such as static state estimation in power transmission systems. The dynamic nature of distributed generation (i.e. solar and wind), vehicle to grid technology (V2G) and false data injection attacks (FDIAs), may pose significant challenges to the application of classical MLPs to state estimation (SE) and state forecasting (SF) in power distribution systems. This paper investigates the application of conventional neural networks (MLPs) and deep learning based models such as convolutional neural networks (CNNs) and long-short term networks …
Application Of Deep Neural Networks To Distribution System State Estimation And Forecasting, James P. Carmichael, Yuan Liao
Application Of Deep Neural Networks To Distribution System State Estimation And Forecasting, James P. Carmichael, Yuan Liao
Electrical and Computer Engineering Faculty Publications
Classical neural networks such as feedforward multi-layer perceptron models (MLPs) are well established as universal approximators and as such, show promise in applications such as static state estimation in power transmission systems. The dynamic nature of distributed generation (i.e. solar and wind), vehicle to grid technology (V2G) and false data injection attacks (FDIAs), may pose significant challenges to the application of classical MLPs to state estimation (SE) and state forecasting (SF) in power distribution systems. This paper investigates the application of conventional neural networks (MLPs) and deep learning based models such as convolutional neural networks (CNNs) and long-short term networks …
A Deep Neural Network For Early Detection And Prediction Of Chronic Kidney Disease, Vijendra Singh, Vijayan K. Asari, Rajkumar Rajasekaran
A Deep Neural Network For Early Detection And Prediction Of Chronic Kidney Disease, Vijendra Singh, Vijayan K. Asari, Rajkumar Rajasekaran
Electrical and Computer Engineering Faculty Publications
Diabetes and high blood pressure are the primary causes of Chronic Kidney Disease (CKD). Glomerular Filtration Rate (GFR) and kidney damage markers are used by researchers around the world to identify CKD as a condition that leads to reduced renal function over time. A person with CKD has a higher chance of dying young. Doctors face a difficult task in diagnosing the different diseases linked to CKD at an early stage in order to prevent the disease. This research presents a novel deep learning model for the early detection and prediction of CKD. This research objectives to create a deep …
Circuit Optimization Techniques For Efficient Ex-Situ Training Of Robust Memristor Based Liquid State Machine, Alex Henderson, Christopher Yakopcic, Cory Merkel, Steven Harbour, Tarek M. Taha, Hananel Hazan
Circuit Optimization Techniques For Efficient Ex-Situ Training Of Robust Memristor Based Liquid State Machine, Alex Henderson, Christopher Yakopcic, Cory Merkel, Steven Harbour, Tarek M. Taha, Hananel Hazan
Electrical and Computer Engineering Faculty Publications
Spiking neural network hardware offers a high performance, power-efficient and robust platform for the processing of complex data. Many of these systems require supervised learning, which poses a challenge when using gradient-based algorithms due to the discontinuous properties of SNNs. Memristor based hardware can offer gains in portability, power reduction, and throughput efficiency when compared to pure CMOS. This paper proposes a memristor-based spiking liquid state machine (LSM). The inherent dynamics of the LSM permit the use of supervised learning without backpropagation for weight updates. To carry out the design space evaluation of the LSM for optimal hardware performance, several …
Meltpondnet: A Swin Transformer U-Net For Detection Of Melt Ponds On Arctic Sea Ice, Ivan Sudakow, Vijayan K. Asari, Ruixu Liu, Denis Demchev
Meltpondnet: A Swin Transformer U-Net For Detection Of Melt Ponds On Arctic Sea Ice, Ivan Sudakow, Vijayan K. Asari, Ruixu Liu, Denis Demchev
Electrical and Computer Engineering Faculty Publications
High-resolution aerial photographs of Arctic region are a great source for different sea ice feature recognition, which are crucial to validate, tune, and improve climate models. Melt ponds on the surface of melting Arctic sea ice are of particular interest as they are sensitive and valuable indicators and are proxy to the processes in the Arctic climate system. Manual analysis of this remote sensing data is extremely difficult and time-consuming due to the complex shapes and unpredictable boundaries of the melt ponds, and that leads to the necessity for automatizing the processes. In this study, we propose a robust and …
A Progressive Learning Strategy For Large-Scale Glacier Mapping, Zhiyuan Xie, Umesh K. Haritashya, Vijayan K. Asari
A Progressive Learning Strategy For Large-Scale Glacier Mapping, Zhiyuan Xie, Umesh K. Haritashya, Vijayan K. Asari
Electrical and Computer Engineering Faculty Publications
In recent years, the worldwide temperature increase has resulted in rapid deglaciation and a higher risk of glacier-related natural hazards such as flooding and debris flow. Due to the severity of these hazards, continuous observation and detailed analysis of glacier fluctuations are crucial. Many such analyses require an accurately delineated glacier boundary. However, the complexity and heterogeneity of glaciers, particularly debris-covered glaciers (DCGs), poses a challenge for glacier mapping when using conventional remote sensing or machine-learning techniques. Some examples exist about small-scale automated glacier mapping, but large or regional-scale mapping is challenging. Previously, a deep-learning-based approach named GlacierNet2 had been …
Nondestructive Detection Of Codling Moth Infestation In Apples Using Pixel-Based Nir Hyperspectral Imaging With Machine Learning And Feature Selection, Nader Ekramirad, Alfadhl Y. Khaled, Lauren E. Doyle, Julia R. Loeb, Kevin D. Donohue, Raul T. Villanueva, Akinbode A. Adedeji
Nondestructive Detection Of Codling Moth Infestation In Apples Using Pixel-Based Nir Hyperspectral Imaging With Machine Learning And Feature Selection, Nader Ekramirad, Alfadhl Y. Khaled, Lauren E. Doyle, Julia R. Loeb, Kevin D. Donohue, Raul T. Villanueva, Akinbode A. Adedeji
Electrical and Computer Engineering Faculty Publications
Codling moth (CM) (Cydia pomonella L.), a devastating pest, creates a serious issue for apple production and marketing in apple-producing countries. Therefore, effective nondestructive early detection of external and internal defects in CM-infested apples could remarkably prevent postharvest losses and improve the quality of the final product. In this study, near-infrared (NIR) hyperspectral reflectance imaging in the wavelength range of 900–1700 nm was applied to detect CM infestation at the pixel level for three organic apple cultivars, namely Gala, Fuji and Granny Smith. An effective region of interest (ROI) acquisition procedure along with different machine learning and data processing …
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 …
Evaluating Deep-Learning Models For Debris-Covered Glacier Mapping, Zhiyuan Xie, Vijayan K. Asari, Umesh K. Haritashya
Evaluating Deep-Learning Models For Debris-Covered Glacier Mapping, Zhiyuan Xie, Vijayan K. Asari, Umesh K. Haritashya
Electrical and Computer Engineering Faculty Publications
In recent decades, mountain glaciers have experienced the impact of climate change in the form of accelerated glacier retreat and other glacier-related hazards such as mass wasting and glacier lake outburst floods. Since there are wide-ranging societal consequences of glacier retreat and hazards, monitoring these glaciers as accurately and repeatedly as possible is important. However, the accurate glacier boundary, especially the debriscovered glacier (DCG) boundary, which is one of the primary inputs in many glacier analyses, remains a challenge even after many years of research using conventional remote sensing methods or machine-learning methods. The GlacierNet, a deep-learning-based approach, utilized the …
Ee-Acml: Energy-Efficient Adiabatic Cmos/Mtj Logic For Cpa-Resistant Iot Devices, Zachary Kahleifeh, Himanshu Thapliyal
Ee-Acml: Energy-Efficient Adiabatic Cmos/Mtj Logic For Cpa-Resistant Iot Devices, Zachary Kahleifeh, Himanshu Thapliyal
Electrical and Computer Engineering Faculty Publications
Internet of Things (IoT) devices have strict energy constraints as they often operate on a battery supply. The cryptographic operations within IoT devices consume substantial energy and are vulnerable to a class of hardware attacks known as side-channel attacks. To reduce the energy consumption and defend against side-channel attacks, we propose combining adiabatic logic and Magnetic Tunnel Junctions to form our novel Energy Efficient-Adiabatic CMOS/MTJ Logic (EE-ACML). EE-ACML is shown to be both low energy and secure when compared to existing CMOS/MTJ architectures. EE-ACML reduces dynamic energy consumption with adiabatic logic, while MTJs reduce the leakage power of a circuit. …
Artificial Intelligence Method For The Forecast And Separation Of Total And Hvac Loads With Application To Energy Management Of Smart And Nze Homes, Rosemary E. Alden, Huangjie Gong, Evan S. Jones, Cristinel Ababei, Dan M. Ionel
Artificial Intelligence Method For The Forecast And Separation Of Total And Hvac Loads With Application To Energy Management Of Smart And Nze Homes, Rosemary E. Alden, Huangjie Gong, Evan S. Jones, Cristinel Ababei, Dan M. Ionel
Electrical and Computer Engineering Faculty Publications
Separating the HVAC energy use from the total residential load can be used to improve energy usage monitoring and to enhance the house energy management systems (HEMS) for existing houses that do not have dedicated HVAC circuits. In this paper, a novel method is proposed to separate the HVAC dominant load component from the house load. The proposed method utilizes deep learning techniques and the physical relationship between HVAC energy use and weather. It employs novel long short-term memory (LSTM) encoder-decoder machine learning (ML) models, which are developed based on future weather data input in place of weather forecasts. In …