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Soner Onder Receives Year One Funding for $1.2M NSF SCALE Project

Published September 13, 2019

Soner Onder, professor of computer science, was recently awarded $246,329 for the first year of a four-year NSF grant for his project, “SHF: Medium: Collaborative Research: Statically Controlled Asynchronous Lane Execution (SCALE).” The project is in collaboration with Prof. David Whalley of Florida State University. Michigan Tech is the lead institution in the project, it is expected to total $1.2 million, with Michigan Tech receiving $600,000.

Abstract: Enabling better performing systems benefits applications that span those running on mobile devices to large data applications running on data centers. The efficiency of most applications is still primarily affected by single thread performance. Instruction-level parallelism (ILP) speeds up programs by executing instructions of the program in parallel, with ‘superscalar’ processors achieving maximum performance. At the same time, energy efficiency is a key criteria to keep in mind as such speedup happens, with these two being conflicting criteria in system design. This project develops a Statically Controlled Asynchronous Lane Execution (SCALE) approach that has the potential to meet or exceed the performance of a traditional superscalar processor while approaching the energy efficiency of a very long instruction word (VLIW) processor. As implied by its name, the SCALE approach has the ability to scale to different types and levels of parallelism. The toolset and designs developed in this project will be available as open-source and will also have an impact on both education and research. The SCALE architectural and compiler techniques will be included in undergraduate and graduate curricula.

The SCALE approach supports separate asynchronous execution lanes where dependencies between instructions in different lanes are statically identified by the compiler to provide inter-lane synchronization. Providing distinct lanes of instructions allows the compiler to generate code for different modes of execution to adapt to the type of parallelism that is available at each point within an application. These execution modes include explicit packaging of parallel instructions, parallel and pipelined execution of loop iterations, single program multiple data (SPMD) execution, and independent multi-threading.

This award reflects NSF’s statutory mission and has been deemed worthy of support through evaluation using the Foundation’s intellectual merit and broader impacts review criteria.

https://www.nsf.gov/awardsearch/showAward?AWD_ID=1901005&HistoricalAwards=false

Keith Vertanen and Scott Kuhl Awarded $500K NSF Grant

Published September 5, 2019

By Karen Johnson

Keith Vertanen, assistant professor of computer science (HCC), and Scott Kuhl (HCC), associate professor of computer science, are principal investigators of a recently funded three-year National Science Foundation grant for their project, “CHS: Small: Rich Surface Interaction for Augmented Environments.” The expected funding over three years is $499,552.00.

Vertanen and Kuhl are members of Michigan Tech’s Institute of Computing and Cybersystems (ICC) Center for Human-Centered Computing. A 2018 ICC research seed grant funded by ECE Alumnus Paul Williams was used to produce some of the preliminary results in the successful proposal. More info about the Williams Seed Grant can be found here: https://blogs.mtu.edu/icc/2019/07/16/appropriating-everyday-surfaces-for-tap-interaction/.

A related video can be found here: https://youtu.be/sF7aeXMfsIQ.

Abstract: Virtual Reality (VR) and Augmented Reality (AR) head-mounted displays are increasingly being used in different computing related activities such as data visualization, education, and training. Currently, VR and AR devices lack efficient and ergonomic ways to perform common desktop interactions such as pointing-and-clicking and entering text. The goal of this project is to transform flat, everyday surfaces into a rich interactive surface. For example, a desk or a wall could be transformed into a virtual keyboard. Flat surfaces afford not only haptic feedback, but also provide ergonomic advantages by providing a place to rest your arms. This project will develop a system where microphones are placed on surfaces to enable the sensing of when and where a tap has occurred. Further, the system aims to differentiate different types of touch interactions such as tapping with a fingernail, tapping with a finger pad, or making short swipe gestures.

This project will investigate different machine learning algorithms for producing a continuous coordinate for taps on a surface along with associated error bars. Using the confidence of sensed taps, the project will investigate ways to intelligently inform aspects of the user interface, e.g. guiding the autocorrection algorithm of a virtual keyboard decoder. Initially, the project will investigate sensing via an array of surface-mounted microphones and design “surface algorithms” to determine and compare the location accuracy of the finger taps on the virtual keyboard. These algorithms will experiment with different models including existing time-of-flight model, a new model based on Gaussian Process Regression, and a baseline of classification using support vector machines. For all models, the project will investigate the impact of the amount of training data from other users, and varying the amount of adaptation data from the target user. The project will compare surface microphones with approaches utilizing cameras and wrist-based inertial sensors. The project will generate human-factors results on the accuracy, user preference, and ergonomics of interacting midair versus on a rigid surface. By examining different sensors, input surfaces, and interface designs, the project will map the design space for future AR and VR interactive systems. The project will disseminate software and data allowing others to outfit tables or walls with microphones to enable rich interactive experiences.

GenCyber Camp for Teachers Garners Local Media Coverage

Published August 20, 2019

By Karen Johnson

Michigan Tech hosted two week-long GenCyber camps this summer. The first, held June 17–21, 2019, hosted 30 local middle/high school students. The second camp, August 12–16, 2019, hosted 21 local K-12 teachers. Camp participants gained cybersecurity knowledge, understood correct and safe online behavior, and explored ways to deliver cybersecurity content in K-12 curricula.

A story about the GenCyber teacher camp was reported on August 16, 2019, by TV6: “GenCyber cyber security training camp comes to Michigan Tech” and on August 13, 2019, by the Keweenaw Report: “Teachers Learn How To Include Cybersecurity In Their Lessons.”

Learn more about the camps on the Institute of Computing and Cybersystems blog: https://blogs.mtu.edu/icc/2019/06/04/inspiring-the-next-generation-of-cyber-stars-2/.

Susanta Ghosh is PI on $170K NSF Grant

Published July 30, 2019

By Karen Johnson

Susanta Ghosh (ICC-DataS/MEEM/MuSTI) is Principal Investigator on a project that has received a $170,604 research and development grant from the National Science Foundation. The project is titled “EAGER: An Atomistic-Continuum Formulation for the Mechanics of Monolayer Transition Metal Dichalcogenides.” This is a potential 19-month project.

Dr. Ghosh is an assistant professor of Mechanical Engineering-Engineering Mechanics at Michigan Tech. Before joining the Michigan Tech College pof Engineering, Dr. Ghosh was an associate in research in the Pratt School of Engineering at Duke University; a postdoctoral scholar in the departments of Aerospace Engineering and Materials Science & Engineering at the University of Michigan, Ann Arbor; and a research fellow at the Technical University of Catalunya, Barcelona, Spain. His M.S. and Ph.D. degrees are from the Indian Institute of Science (IISc), Bangalore. His research interests include multi-scale solid mechanics, atomistic modeling, ultrasound elastography, and inverse problem and computational science.

Abstract: Two-dimensional materials are made of chemical elements or compounds of elements while maintaining a single atomic layer crystalline structure. Two-dimensional materials, especially Transition Metal Dichalcogenides (TMDs), have shown tremendous promise to be transformed into advanced material systems and devices, e.g., field-effect transistors, solar cells, photodetectors, fuel cells, sensors, and transparent flexible displays. To achieve broader use of TMDs across cutting-edge applications, complex deformations for large-area TMDs must be better understood. Large-area TMDs can be simulated and analyzed through predictive modeling, a capability that is currently lacking. This EArly-concept Grant for Exploratory Research (EAGER) award supports fundamental research that overcomes current challenges in large-scale atomistic modeling to obtain an efficient but reliable continuum model for single-layer TMDs containing billions of atoms. The model will be translational and will contribute towards the development of a wide range of applications in the nanotechnology, electronics, and alternative energy industries. The award will further support development of an advanced graduate-level course on multiscale modeling and organization of symposia in two international conferences on mechanics of two-dimensional materials. Experimental samples of TMDs contain billions of atoms and hence are inaccessible to the state-of-the-art molecular dynamics simulations. Moreover, existing crystal elastic models for surfaces cannot be applied to multi-atom thick 2D TMDs due to the presence of interatomic bonds across the atomic surfaces. The crystal elastic model aims to solve this problem by projecting all interatomic bonds onto the mid-surface to track their deformations. The actual deformed bonds will, therefore, be computed using the deformations of the mid-surface. Additionally, a technique will be derived to incorporate the effects of curvature and stretching of TMDs on their interactions with substrates. The model will be exercised to generate insights into the mechanical instabilities and the role of substrate interactions on them. The coarse-grained model will overcome the computational bottleneck of molecular dynamics models to simulate TMDs samples comprising billions of atoms. This award reflects NSF’s statutory mission and has been deemed worthy of support through evaluation using the Foundation’s intellectual merit and broader impacts review criteria.

Scalable Spectral Sparsification of Graph Laplacians and Integrated Circuits

Published July 16, 2019

By Karen Johnson

Researcher: Zhuo Feng, Associate Professor, Electrical and Computer Engineering

Sponsor: National Science Foundation: SHF: Small

Amount of Support: $450,000

Duration of Support: 3 years

Abstract: This research is motivated by investigations on scalable methods for design simplifications of nanoscale integrated circuits (ICs). This is to be achieved by extending the associated spectral graph sparsification framework to handle Laplacian-like matrices derived from general nonlinear IC modeling and simulation problems. The results from this research may prove to be key to the development of highly scalable computer-aided design algorithms for modeling, simulation, design, optimization, as well as verification of future nanoscale ICs that can easily involve multi-billions of circuit components. The algorithms and methodologies developed will be disseminated to leading technology companies that may include semiconductor and Electronic Design Automation companies as well as social and network companies, for potential industrial deployments.

Spectral graph sparsification aims to find an ultra-sparse subgraph (a.k.a. sparsifier) such that its Laplacian can well approximate the original one in terms of its eigenvalues and eigenvectors. Since spectrally similar subgraphs can approximately preserve the distances, much faster numerical and graph-based algorithms can be developed based on these “spectrally” sparsified networks. A nearly-linear complexity spectral graph sparsification algorithm is to be developed based on a spectral perturbation approach. The proposed method is highly scalable and thus can be immediately leveraged for the development of nearly-linear time sparse matrix solvers and spectral graph (data) partitioning (clustering) algorithms for large real-world graph problems in general. The results of the research may also influence a broad range of computer science and engineering problems related to complex system/network modeling, numerical linear algebra, optimization, machine learning, computational fluid dynamics, transportation and social networks, etc.

More details.

Improving Reliability of In-Memory Storage

Published July 16, 2019

By Karen Johnson

Researcher: Jianhui Yue, PI, Assistant Professor, Computer Science

Sponsor: National Science Foundation, SHF: Small: Collaborative Research

Amount of Support: $192, 716

Duration of Support: 3 years

Abstract: Emerging nonvolatile memory (NVM) technologies, such as PCM, STT-RAM, and memristors, provide not only byte-addressability, low-latency reads and writes comparable to DRAM, but also persistent writes and potentially large storage capacity like an SSD. These advantages make NVM likely to be next-generation fast persistent storage for massive data, referred to as in-memory storage. Yet, NVM-based storage has two challenges: (1) Memory cells have limited write endurance (i.e., the total number of program/erase cycles per cell); (2) NVM has to remain in a consistent state in the event of a system crash or power loss. The goal of this project is to develop an efficient in-memory storage framework that addresses these two challenges. This project will take a holistic approach, spanning from low-level architecture design to high-level OS management, to optimize the reliability, performance, and manageability of in-memory storage. The technical approach will involve understanding the implication and impact of the write endurance issue when cutting-edge NVM is adopted into storage systems. The improved understanding will motivate and aid the design of cost-effective methods to improve the life-time of in-memory storage and to achieve efficient and reliable consistence maintenance.

Publications:

Pai Chen, Jianhui Yue, Xiaofei Liao, Hai Jin. “Optimizing DRAM Cache by a Trade-off between Hit Rate and Hit Latency,” IEEE Transactions on Emerging Topics in Computing, 2018. doi:10.1109/TETC.2018.2800721

Chenlei Tang, Jiguang Wan, Yifeng Zhu, Zhiyuan Liu, Peng Xu, Fei Wu and Changsheng Xie. “RAFS: A RAID-Aware File System to Reduce Parity Update Overhead for SSD RAID,” Design Automation Test In Europe Conference (DATE) 2019, 2019.

Pai Chen, Jianhui Yue, Xiaofei Liao, Hai Jin. “Trade-off between Hit Rate and Hit Latency for Optimizing DRAM Cache,” IEEE Transactions on Emerging Topics in Computing, 2018.

More details

Zhou Feng is PI on $500K NSF Project

Published June 21, 2019

By Karen Johnson

Zhuo Feng (ECE/ICC) is Principal Investigator on a project that has received a $500,000 research and development grant from the National Science Foundation. This potential three-year project is titled, “SHF: Small: Spectral Reduction of Large Graphs and Circuit Networks.”

This research project will investigate a truly-scalable yet unified spectral graph reduction approach that allows reducing large-scale, real-world directed and undirected graphs with guaranteed preservation of the original graph spectra. Unlike prior methods that are only suitable for handling specific types of graphs (e.g. undirected or strongly-connected graphs), this project uses a more universal approach and thus will allow for spectral reduction of a much wider range of real-world graphs that may involve billions of elements:

spectrally-reduced social (data) networks allow for more efficiently modeling, mining and analysis of large social (data) networks;
spectrally-reduced neural networks allow for more scalable model training and processing in emerging machine learning tasks;
spectrally-reduced web-graphs allow for much faster computations of personalized PageRank vectors;
spectrally-reduced integrated circuit networks will lead to more efficient partitioning, modeling, simulation, optimization and verification of large chip designs, etc.

From Tech Today, June 21, 2019

Inspiring the Next Generation of Cyber Stars

Published June 4, 2019

By Karen Johnson

Yu Cai GenCyber Logo By Karen S. Johnson, ICC Communications Director

We live in a world where pretty much everything and everybody – individuals, companies, governments, critical infrastructure – are increasingly dependent on connected systems, networks and devices. And, as newspaper headlines reveal, those systems may be insecure and vulnerable to hackers.

“Nowadays, everybody is using computers, and more and more things are connected. That provides convenience, flexibility, a lot of great things, but it also opens the doors for hackers,” says Yu Cai, associate professor and program chair for the Computer Network and System Administration program at Michigan Technological University.

“The world has increasingly become a combination of the physical world and the cyber world,” Cai adds. “That’s why cybersecurity is important, because you want to protect yourself. As human beings, we evolved over thousands of years to take care of our security in the physical world. But in the cyber world, many don’t have a very good idea of how to protect themselves.”

Cai is principal investigator on two grant awards, each for about $85K, which are making possible two free, non-residential, week-long GenCyber summer camps on Michigan Tech’s campus. The first camp, for middle school and high school students, is the week of June 17. The second camp, for K-12 STEM teachers, is the week of August 12. Both camps and all learning materials are offered at no cost to camp participants. Each participant will receive a Raspberry Pi minicomputer. Breakfast and lunch are provided. For enrollment information, visit mtu.edu/gencyber.

Funded jointly by the National Security Agency (NSA) and the National Science Foundation (NSF), the goals of the nationwide GenCyber program are to increase interest in cybersecurity careers and diversity in the national cybersecurity workforce, help students understand correct and safe on-line behavior and how they can be good digital citizens, and improve teaching methods for delivery of cybersecurity content in K-12 curricula.

“This is part of our picture to make Michigan Tech a leader in cybersecurity research and education,” Cai says of this summer’s GenCyber camps. “We have other cybersecurity curriculum development grants that focus on college education, now we want to outreach to K through 12.”

In both camp sessions, participants will explore the world of cybersecurity through real-world case studies, hands-on learning activities and games, interactive lectures, career exploration, and field trips. Covered topics include safe online behavior, cyber ethics, fundamental computer and network knowledge, and cybersecurity career options and educational opportunities.

“We’ll also cover common vulnerabilities and weaknesses of computer systems, such as how hackers get into the systems, and how systems can be strengthened to defeat hackers against the hundreds of vulnerabilities,” Cai adds.

Tim Van Wagner, a lecturer at Michigan Tech and a co-PI on the grants, is the lead teacher for the camps. Cai and his other co-PIs—associate professor Guy Hembroff and assistant professor Bo Chen—will also present learning modules and assist with the camps.

K-12 pedagogical expertise in curriculum development was provided by Copper Country Intermediate School District (CCISD) staff members Emily Gochis, Director of the Region 16 MiSTEM Network, and Steve Kass, Educational Technologist.

“Steve and Emily provided a lot of input and suggestions regarding the camp curriculum and advised us in the best practices for teaching high school students,” Cai says, adding that they are also helping to promote the camps in local public schools.

Driving the curriculum are four principles: Learning by Storytelling, Learning by Doing, Learning by Gaming, and Learning by Teaching. Cai and his team will be assessing the effectiveness of these principles using several methods. The resulting research will be shared with the GenCyber program and the public.

The two grants are titled, “Innovative GenCyber Learning Experience for K-12 Teachers Through Storytelling + Teaching + Gaming + Doing” and “Innovative GenCyber Learning Experience for High School Students Through Storytelling + Teaching + Gaming + Doing.”

Yu Cai is PI on $82K NSA/NSF Grant

Published May 20, 2019

By Karen Johnson

Yu Cai Yu Cai (TTEC/ICC) is Principal Investigator on a project that has received a $82,416 Other Sponsored Activities Grant from the National Security Agency/National Science Foundation. The one-year project is titled, “Innovation GenCyber Learning Experience for High School Students Through Storytelling + Teaching + Gaming + Doing.” Bo Chen (SCS), Guy Hembroff (TTEC), and Tim Van Wagner (TTEC), are co-PIs.

Zhaohui Wang Wins NSF CAREER Award

Published April 6, 2017

By Karen Johnson

Zhaohui Wang (CPS) is the recipient of an NSF CAREER Award for her research in underwater communication networks. Wang plans to improve underwater acoustics networks to maximize information delivery. ICC Co-Director, Dan Fuhrmann commented, “Her research activity is quite remarkable. In this proposal, Wang describes an ambitious plan to bring state-of-the-art tools in signal processing and machine learning to the difficult problem of underwater acoustic communication.” Read more about Wang’s research in the Michigan Tech News.

Category: NSF