M. Hassan Najafi Receives EDAA’s Outstanding Dissertations Award for 2018

M. Hassan Najafi has been awarded the European Design and Automation Association’s  2018 Outstanding Dissertations Award for his research contributions to the advancement of design, automation, and test. Established to promote the work of young researchers in the field, and to recognize the importance of university research, the award is open to researchers across the world. Hassan, a recent graduate of ECE’s doctoral program, is one of four award winners. The award will be conferred at the Design, Automation and Test in Europe 2019 Conference and Exhibition to be held in Florence, Italy, during the last week of March.

HASSAN’S RESEARCH

Hassan’s dissertation is titled “New Views for Stochastic Computing: From Time-Encoding to Deterministic Processing.” Stochastic computing first emerged in the 1960s, positioned as a new paradigm for emerging technologies and post-CMOS computing, and an alternative to conventional computing. While there are several advantages to stochastic computing, latency of operations, which translates to higher energy consumption, is a noteworthy disadvantage. The cost of bit-stream generators is another important downside.

In his dissertation, Hassan addresses these issues by proposing an unorthodox idea: performing computation with digital constructs on time-encoded analog signals. He presents a new, energy-efficient, high-performance and inexpensive approach for stochastic computing using time-encoded pulse signals. His experimental results have shown several gains: 99% performance speedup, 98% savings in energy loss, and 40% area reduction compared to prior stochastic implementations.

Hassan also addresses achieving progressive precision, another key challenge with deterministic methods of stochastic computing, He proposes a high-quality down sampling method which significantly improves processing time and energy consumption of typical deterministic methods. His research offers two noteworthy firsts: introduction of two novel deterministic methods of processing bit-streams using low-discrepancy sequences, and exploitation of the skew tolerance of stochastic circuits to develop polysynchronous clocking. Most significantly, as an outcome of his research, Hassan presents a seamless stochastic system, StochMem, which uses analog memory to trade the energy and area overhead of data conversion for computation accuracy in stochastic systems.

IMPACT OF HASSAN’S RESEARCH

In the course of his research, Hassan developed new design methodologies, and new research directions to the stochastic computing and unary processing fields. His work has established some counterintuitive and fundamental new design methodologies for the design of digital stochastic systems. The results of his research have challenged the perceived limitations of stochastic computing, while simultaneously paving a path to designing significantly smaller, faster, and energy-efficient embedded systems.

AWARDS AND HONORS

Hassan has been a recipient of several awards and honors during his time as a student in the Department of Electrical and Computer Engineering. Most recently, he received the University’s 2017-2018 Doctoral Dissertation Fellowship Award. The dissertation fellowship is a University-wide award that supports top doctoral candidates.

His work on polysynchronous stochastic circuits was selected as the Feature Paper of the Month in the October 2017 issue of IEEE Transactions on Computers. His work on high-quality down-sampling of deterministic bit-streams was presented in the Best Papers Session of the 35th IEEE International Conference on Computer Design (ICCD 2017) and received the Best Paper Award of the conference. The paper was also among the top-ranked papers of the conference for publication in IEEE Transactions on Emerging Topics in Computing: Special Issue on Emerging Topics in Computer Design.

M. Hassan Najafi earned his doctoral degree in electrical engineering at the University of Minnesota in 2018, under the guidance of Prof. David Lilja. Currently, he is an Assistant Professor at the Center for Advanced Computer Studies at the University of Louisiana at Lafayette. Learn more about Hassan’s research here.







Prof. Soheil Mohajer Named 2019-2021 McKnight Land-Grant Professor

Prof. Soheil Mohajer has been named McKnight Land-Grant Professor for his work addressing “Challenges in Distributed Systems for Big Data Analysis.” He is one of ten awardees of the McKnight Land-Grant Professorship Program for 2019-21. His areas of research expertise include communications, signal processing, and networking. He works on developing technologies for storage, transmission, and processing of massive amounts of data in a distributed fashion, using mathematical analysis to design algorithms that are practical and reliable for distributed architectures.

Soheil leads a research team that addresses some of the challenges that have sprung from the rapid growth, and usage of digital data. With the explosion in data, its processing, storage, maintenance, transmission, and security have turned into a multibillion dollar industry. Two key challenges he and his research team are working on are, storage solutions that can cope with the data explosion, and transmission of such massive amounts of data on networks. With the prevailing shift in models, from centralized architectures to decentralized architectures, for data-driven applications, he and his team are addressing these challenges with theoretical and algorithmic solutions.

Distributed Data Storage Systems and Innovative Coding Techniques

At issue, for researchers, and practitioners (such as data storage firms), is the question of developing a cost-efficient storage system that can cope with the explosion in data. For Soheil and his team, the answer lies in harnessing the strength of distributed storage systems (DSS). Such a system, created by networking a large number of inexpensive devices, could be the solution to storing the massive amounts of data that are constantly being generated. To tackle the problem of unreliability or failure within the constituents of a network, a typical strategy is to build in redundancy. However, the conventional form of redundancy, which involves the replication of data in multiple storage nodes, is an expensive measure, when you account for hardware and maintenance costs of additional storage units. Soheil’s team however have turned to coding techniques, which when combined with hardware redundancy can offer greater reliability at the same hardware cost.

Innovative Coding Techniques

Coding techniques comprise algorithms for reconstructing data. To address the problem of storage node failures with coding techniques, the missing data is recovered by performing some operations on the repair data which are downloaded from several nodes. But some of the barriers to the successful and widespread implementation of coding techniques for repair are the computation and communication costs associated with them. Soheil’s team are working on addressing these barriers by developing high-performance codes for storage systems and efficient algorithms for data recovery. Their algorithms are also scalable which make them versatile regardless of changes to the size of the storage network. With sensitive data such as personal, financial, and health records also increasingly moving to cloud storage, the team is also working on the design of secure cloud storage systems to ensure data confidentiality and integrity. The algorithms they have developed in strike a balance between the necessary levels of security, fault tolerance, system maintenance, and performance. (Soheil’s research on cloud storage systems is supported by the NSF CAREER Award he received in 2018.)

Innovative Data Delivery

As access to and use of the Internet has evolved, demand for broadband data has grown exponentially. The nature of data travelling across the network has also changed to typically include large files such as videos, as opposed to short messages which were once the norm. Besides, these data requests are accompanied by specific characteristics such as repeat requests, specific request times, and most frequently requested files. Services such as Netflix are an excellent example of such requests. Most people will typically access movies at night, there might be multiple requests for the same file/movie, and certain types of content might be requested more frequently than others (newly released movies versus older releases).

Caching, based on predicting what content might be more popular as compared to others, is one way to reduce network traffic. However, its effectiveness is limited by the size of the local memory of each user. Another critical disadvantage is its irrelevance when users request other files. But Soheil and his research team are taking the concept of caching one step further by exploiting the ubiquity of mobile devices. Using the considerable combined storage that these devices present, they suggest coded caching as a novel solution to data delivery.

Coded caching operates as if each user will have access to the cache of other users in the network. The strategy presents some key advantages: reduction in network traffic, and provision of internet access in rural and semi-rural areas affected by poor infrastructure. Currently Soheil is working on developing algorithms that will make coded caching practical and implementable. These include algorithms that can determine what kinds of data have to be cached, and delivered to minimize network load during peak hours.  

Prof. Soheil Mohajer’s research is of critical value, for individuals as well as corporations. His work has been supported by the National Science Foundation; he was a recipient of the Early CAREER Award in 2018 for the study of distributed computing networks and the development of models for data transfer that can reduce time delay in communication.

The McKnight Land-Grant Professorship Program was set up to advance the careers of assistant professors at a critical juncture in their professional lives. Award recipients hold the title of McKnight Land-Grant Professor for two years. Administered by the Office of the Executive Vice-President and Provost, the awards are made possible by generous donations from the McKnight Foundation.

Prof. Soheil Mohajer’s research lab

Prof. Soheil Mohajer’s ECE profile page

Spotlight on Recipients of the Kevin and AJ KleinOsowski and Gary H. Glover Fellowships

Susmita Dey Manasi, Zamshed Chowdhury, and Omer Demirel are some of the recipients of the KleinOsowski, and Glover Fellowships. Here, we shine a spotlight on their research interests, the impact of their work, and how the fellowships have affected their University experience. 

Susmita Dey Manasi (KleinOsowski 2017 – 2018)

The Kevin and AJ KleinOsowski fellowship not only supported me financially, but has also been a great motivator for me as I entered the doctoral program in the electrical engineering program. The fellowship has allowed me the luxury of staying focused on my research.


Doctoral student Susmita Dey Manasi

My name is Susmita Dey Manasi and I am pursuing a doctoral degree in the Department of Electrical and Computer Engineering, under the supervision of Prof. Sachin Sapatnekar. I am from Bangladesh where I earned my bachelor of science degree from the Bangladesh University of Engineering and Technology. My research interest lies in the design and optimization of VLSI circuits and systems. I am also interested in neuromorphic computing, approximate computing, beyond-CMOS technologies, as well as device-circuit and architectural co-design for developing efficient VLSI systems.

I chose the University of Minnesota after rigorously exploring the research initiatives in my areas of interest, as offered by several universities. The state-of-the-art research facilities, and outstanding faculty profiles relevant to my interests and experience, are what motivated me to apply to this University.

Deep learning (DL) networks have drawn a lot of attention because of the promise they hold for brain-inspired intelligence in electronics. Currently, DL techniques have a wide range of applications such as object classification, speech recognition, face detection, web search, natural language processing, driverless car, and others. However, DL tasks are computationally expensive. To enable widespread deployment of DL techniques, especially on resource-limited embedded platforms, the development of energy efficient hardware systems for DL applications is critical. Currently, I am working on DL hardware systems to enable low energy computation for DL networks. As part of my research, I have developed an analytical model to estimate neural computation energy on a customized hardware platform and utilized the model to determine energy optimal computation scheduling for DL tasks on mobile platform. Currently, I am looking into various architectural alternatives of embedded DL processors to design optimal hardware set for various DL applications.

After earning my doctoral degree, I see myself as an academic conducting research at a world-class institution, or as a scientist in a leading research lab.

Zamshed Chowdhury (KleinOsowski 2016 – 2017)

Being awarded the Kevin and AJ KleinOsowski fellowship was incredibly helpful to me. It allowed me to concentrate on my research and studies, and succeed in an educational system that is significantly different from the one I am familiar with. The fellowship was also vital in my decision to attend the University, as it helped to defray the cost of attendance.


Doctoral student Zamshed Chowdhury

My name is Zamshed Chowdhury, and I earned my bachelor’s and master’s degrees in applied physics, electronics, and communication engineering from the University of Dhaka in Bangladesh. Currently, I am pursuing my doctoral degree under the guidance of Prof. Ulya Karpuzcu. My research interests include emerging memory technology, high speed and energy efficient computing, and application specific hardware accelerator design.

My decision to apply to the University of Minnesota was driven by the excellence of the research groups in my areas of research interest. And the award of the KleinOsowski fellowship helped me finalize my school and program choice.

My current research projects involve designing in-memory processing solutions using emerging memory technologies. The goal is to reduce overheads due to expensive memory transfer in processing large datasets, which will make the processing of such datasets faster and energy-efficient. Such a design will also potentially address the approaching limits of Moore’s law. I was engaged in teaching for a while, back in Bangladesh. I plan to go back to teaching after graduation.

Omer Demirel (Glover 2017 – 2018)

The Gary H. Glover fellowship has been instrumental in my decision to pursue my graduate education at the University of Minnesota as opposed to other schools. Knowing that the fellowship awards process is highly competitive, receiving the Glover fellowship was confidence inducing. Critically, the fellowship provided me with a more financially viable year.


Doctoral student Omer Demirel

My name is Omer Demirel. I am from Turkey and I am a second year doctoral student in electrical engineering working under the supervision of Prof. Mehmet Akçakaya. I earned my bachelor’s and master’s degrees in electrical engineering from Bilkent University, where I also started research on Magnetic Particle Imaging.


My research interests lie in the field of magnetic resonance imaging (MRI), specifically image reconstruction algorithms for accelerated imaging, for instance cardiac imaging. Currently I am working on new image reconstruction methods that can reduce scan times without deteriorating image quality. My goal is to accurately diagnose cardiac diseases using novel techniques. The opportunities for cross-disciplinary research that the University of Minnesota offers was what drove me to apply to the doctoral program here. The department and faculty collaboration with the University’s Center for Magnetic Resonance Research (CMRR) which provides expertise in imaging applications, is a unique learning environment and opportunity for me.

On graduating, I would like to pursue a career in academia; my goal is to become a professor and establish my own lab, ideally in MRI research.

Chunhui Dai Receives First Place in NSF Student Research Poster Competition

Doctoral student Chunhui Dai has been awarded first place in the National Science Foundation student research poster competition. The competition was held at the 2018 International Mechanical Engineering Congress and Exposition (IMECE) in Pittsburgh, PA in November. The poster is titled “In-Situ Monitored Self-Assembly of 3D Graphene-based Nanostructures.” Chunhui’s poster was selected from more than 100 posters that participated in the competition. He is conducting his research under the guidance of Prof. Jeong Hyun Cho.

Chunhui with award-winning poster

The technique Chunhui demonstrates in the poster has the potential to be used for rapid and ultra-sensitive detection of biological analytes. It can be applied in medical diagnostics, environmental monitoring, and food safety.

Molecular sensing provides critical information about chemical and physiological processes, which plays a key role in disease detection and treatment. Graphene has recently demonstrated the ability to confine light near its surface and react with an attached molecule, thereby generating a detectable infrared signal. However, as graphene is a two-dimensional material, any sensing activities are restricted to its surface, thus limiting its overall sensitivity. Inspired by the art of origami, Chunhui’s research interest lies in developing a self-assembly process to fold 2D graphene into 3D graphene-based nanostructures, which have the potential to achieve greater light confinement, thereby increasing their sensitivity.

Currently, Chunhui is working on using these structures to analyze haemoglobin for detecting diseases. And in conjunction with other members of Prof. Cho’s research team, he has submitted a proposal to use this technique to study circulating tumor DNA for predicting cancer.

Chunhui received his bachelor’s degree in electrical engineering from State University of New York, Binghamton in 2014, after which he commenced his doctoral program in ECE. Fabrication of 3D nanostructures, the prospect of working with Prof. Cho in this area, and the potential to pursue cross-disciplinary research, drew Chunhui to the University.

Internet of Things Student Project Showcase

Students display their understanding of software development and hardware skills acquired in class

Every semester, during the last week of classes, there is the usual feverish studying for finals week, and turning in the last of the assignments. But amidst this frenzy there is also a quiet excitement tinged with pride that is radiated by the EE 1301 class, as they prepare for the Internet of Things showcase.

The showcase is a culmination of the semester-long four credit course EE 1301: Introduction to Computing Systems. The course introduces students to programming in general, and specifically using C/C++, emphasizing applications in microcontrollers, physical computing, and the Internet of Things (the Internet of Things is where the world of analog, of devices, appliances, vehicles, tools, are connected to the Internet, and can be controlled or activated remotely by devices such as a laptop or smartphone). While students engage in some intensive conceptual learning in the class, the strong hands-on, practical component of the class is what excites them most. In keeping with that, the final deliverable for EE 1301 is an open-ended student-directed group project, which the groups publicly display at the ECE IoT Showcase.

Some of the projects students have created include an internet Connect 4 game with an LED display, a sign language interpreting glove, LED-matrix Breakout game, an elder-care monitoring system, and a pet cooling blanket.


Besides the core concepts, by the end of the semester, students have also developed some transferable skills such as developing and pitching a project concept, working in teams, and debugging combined hardware and software problems.

Given the conceptual and experiential nature of the course, not surprisingly, student feedback on the course has been positive. They are eager to take ownership of their ideas, see it to fulfillment, and proudly exhibit it at the showcase at the semester’s end. ECE faculty such as Kia Bazargan, David Lilja, David Orser, and John Sartori who teach the class, view the driving force of the course to be the opportunity for students to use their creativity and personal interests to develop a new and exciting project concept. They then use the hardware and software development skills learned in class to build something entirely new.

One of Prof. Sartori’s students, referring to a job he landed based on his EE 1301 project, had this to say about the course: I would not have gotten interested in IoT or machine learning, started MKono (the hobby project you encouraged me to start), or known about IoTHackDay. I have you to thank for this job.


The IoT showcase is held during the last week of classes, every fall and spring semester, on the third floor atrium of Keller Hall. Stop by and check our students’ work; the showcase is free and open to the public.

 Interested in finding what our students in advanced classes build? Here are some examples of projects undertaken by students of EE 2361

Frequency Analyzer
3D Scanning Lidar

Tetris Game Console

Prof. Rhonda Franklin Receives IEEE MTT-S’ N. Walter Cox Award

In recognition of her exemplary service to the IEEE Microwave Theory and Techniques Society, Prof. Rhonda Franklin is the 2019 recipient of the N. Walter Cox Award. The citation reads: “The award recognizes an individual who has given exemplary service to the Society in a spirit of selfless dedication and cooperation. The award is instituted in memory N. Walter Cox, a longstanding MTT-S volunteer.”

For Prof. Franklin, the award is a timely acknowledgement of her work as an active and engaged member and volunteer of the MTT-S group. As a researcher, educator, and volunteer, she has displayed her leadership, and selfless spirit in all her undertakings. Her active engagement with the Society began in 1996, at the start of her professional career. Since then, she has undertaken a variety of tasks ranging from reviewing technical papers for the Society’s journals, serving as editor in various capacities, serving on the International Microwave Symposium (IMS) steering committee, several sub-committees, judge for several student paper competitions, and organizer for various workshops and panels. She has also served as a scholarship chair, and has been elected to serve as chair of the Technical Coordinating Committee for Packaging, Integration, and Manufacturing starting in 2019.

However, besides these activities, what has been most profoundly significant for Prof. Franklin and for the many individuals she has influenced, is her work with minority and women students, developing and encouraging their interest in microwave theory, facilitating research and workplace skills development, and actively mentoring them to successfully take on leadership positions in engineering and academia.

Prof. Franklin is driven to inspire, encourage, and empower minority and women students to be the future generation of engineers, scientists and leaders.

PROF. FRANKLIN AS MENTOR

In 2014, Prof. Franklin co-founded IMS Project Connect which is aimed at familiarizing undergraduate and first year minority and women students with the microwave community and industry by facilitating collaboration with the MTT Society through the symposium. She has worked with co-founders, professors Thomas Weller and Rashaunda Henderson, to plan the program which includes developing communication and networking skills, understanding workplace expectations, career opportunities in microwave engineering in industry, academia, and government, and facilitating meetings with industry leaders and scientists. Participants not only engage in these opportunities, but are also expected to turn in a video conveying the import of the experience for them, titled, “IMS through Their Eyes.” A success from its inception, thanks to the strong support and commitment of all volunteers involved, the program is among the first of its kind within IEEE, where a professional society teams up with an undergraduate student education program to impact STEM education.

A leader, mentor, and teacher, Prof. Franklin is eager and enthusiastic to understand how an organization or unit operates, actively listens to ideas and contributions, and works hard to  integrate them to resolve problems, and create new initiatives. These are qualities she has demonstrated as an MTT-S volunteer and researcher, who has enthusiastically encouraged, inspired, and supported future generations of women engineers, and continues to do so each day.

Prof. Franklin’s steady commitment is borne out by awards she has previously received:

  • 2017 John Tate Advising Award
  • 2104 Sara Evans Faculty Scholar/Leader Award
  • 2012 CIC Academic Leadership Fellow
  • 1998 Presidential Early Career Award for Scientists and Engineers (PECASE)
  • 1998 National Science Foundation CAREER award
  • 1998 Professional Engineering Society Council’s Advisor of the Year Award (University of Illinois, Chicago)
  • 1997 Amoco-Silver Circle Award for Teaching Excellence (University of Illinois, Chicago)

And these are just a few of the several she has received. The award will be conferred at the Society’s annual Awards Banquet scheduled during the International Microwave Symposium (June 2-7, 2019) in Boston, Massachusetts. The Department of Electrical and Computer Engineering congratulates Prof. Franklin and thanks her for her selfless service and dedication.

Learn more about Prof. Rhonda Franklin’s research

 

Haoran Sun Receives Best Student Paper Award at Conference on Signals, Systems, and Computers

Doctoral student Haoran Sun is a recipient of the best student paper contest at the 2018 Asilomar Conference on Signals, Systems, and Computers, for the paper “Distributed Non-Convex First-Order Optimization and Information Processing: Lower Complexity Bounds and Rate Optimal Algorithms.” A prestigious prize among signal processing conferences, Haoran’s paper competed against 86 student papers, and came in at third place, among the 8 finalists.

Haoran’s work addresses decentralization of information processing. The next decade will see an estimated 50 billion connected smart devices providing data, services, and ubiquitous real-time information, touching all aspects of our lives, from healthcare to entertainment. Such a scenario necessitates a paradigmatic shift in the way that information processing, computation, and resource management are handled. One promising solution is to move away from the centralized client-server protocol, towards decentralized processing at the network edge. Such decentralization can effectively manage the increasing number of distributed devices and the surge in data, and meet the stringent latency requirements.  

Haoran’s research focuses on such a distributed setting and addresses the question of identifying and achieving the best possible performance for distributed optimization and machine learning. The conference paper presents methods and algorithms capable of using large scale distributed resources such as data and computational power, to perform fast, decentralized, and scalable computation. In the paper, Haoran derives the fundamental performance limits for a class of challenging distributed optimization problems, where multiple nodes collectively optimize certain non-convex functions using local data. He presents an  optimal algorithm, which enables the nodes to find high-quality solutions using the least amount of communication and computational resources.

Haoran Sun earned his Bachelor of Science in Automatic Control from Beijing Institute of Technology, China, in 2015, and earned his Master of Science in Industrial Engineering from Iowa State University in 2017. He is currently pursuing his doctoral degree under the guidance of Prof. Mingyi Hong. His research interests include optimization, machine learning, and its applications in signal processing and wireless communications.

Prof. Jian-Ping Wang to Lead a $10.3 Million Spintronic Materials and Devices Research Center

The University of Minnesota has received funding to the tune of $10.3 million to establish a research center on spintronic materials and devices. The funding comes from National Institute of Standards and Technology (NIST), and its partners in the Nanoelectronic Computing Research (nCORE) consortium, which includes the Semiconductor Research Corporation (SRC), 12 semiconductor industry sponsors, and the National Science Foundation (NSF).

Called Center for Spintronic Materials in Advanced Information Technologies (SMART), it will be led by the University’s Distinguished McKnight University Professor and Robert F. Hartmann Chair in Electrical Engineering, Jian-Ping Wang as director. It will also include researchers from Georgetown University, Massachusetts Institute of Technology (MIT), Pennsylvania State University, and the University of Maryland.

Spintronics (where the spin properties of electrons are harnessed) offers several advantages over conventional electronics such as higher speeds, improved energy efficiency, and greater stability. SMART will bring together experts in spintronic materials and device innovations, which will define new computing paradigms such as neuromorphic computing, probabilistic computing, in-memory computing, and wave-based information processing.

Prof. Wang explains, “Future computation systems will place heavy emphasis on computational paradigms such as neuromorphic structures for cognitive computing, in-memory computing for big-data applications, and reconfigurable structures that are adaptive to changing application needs. These systems will need to be error-resilient and will require high-endurance devices. Spin-based materials and devices provide an ideal platform to satisfy these requirements, and they have been shown to map naturally to these computational paradigms. The inherent non-volatility of spintronic materials, along with the ability to precisely control interactions between them, offer abundant possibilities for developing novel spin devices for a wide variety of information processing needs.”

Future computation systems will have to be error-resilient and will require high-endurance devices. Spin-based materials and devices satisfy these requirements.

To make such devices and bold new paradigms a possibility, spintronic materials have to be further developed and fine-tuned. SMART will be a fully integrated, multi-institutional, and cross-disciplinary program.

Associate director of SMART, Prof. Caroline Ross, Toyota Professor of Materials Science and Engineering at MIT emphasizes the cross-disciplinary nature of the Center. “Driven by the needs of well-defined next-generation computing architectures and paradigms, SMART is a materials-focused research center that also incorporates development of spintronic devices and measurement and metrology techniques.”

The SMART research portfolio is organized around advanced spintronic materials research. The research themes focus on three classes of spintronic materials that have shown exceptional promise in recent years: novel spin-orbit torque materials, ultra-low loss spin-wave materials, and magneto-ionic materials. These themes are supported by principal investigators (PI) performing cross-theme tasks in modeling, state-of-the-art characterization techniques, and a multi-theme focus on developing industry-compatible manufacturing technologies. Collaborations with other SRC centers will develop SMART materials and devices for use in novel computing paradigms.

University of Minnesota successfully housed the STARnet C-SPIN Center in the past five years.

Learn more about Prof. Jian-Ping Wang’s research

Click here for the NIST news release

 

 

 

Prof. Sachin Sapatnekar to lead $5.3 million federal grant to improve electronic circuit design

The University of Minnesota recently received a four-year, $5.3 million grant from the Defense Advanced Research Projects Agency (DARPA), an agency of the U.S. Department of Defense, to lead an effort that could spark the next wave of U.S. semiconductor innovation and broaden the competitive field for circuit design. Integrated circuits power almost every electronic device we use today.

The University of Minnesota is one of only 11 lead universities or companies to receive funding from the DARPA Intelligent Design of Electronic Assets (IDEA)  program, a new program under the DARPA Electronics Resurgence Initiative. Other partners on the University of Minnesota-led grant are Texas A&M University and Intel, a leader in the semiconductor industry.

The complex circuitry in today’s semiconductor chips is built using software that automates the design of analog and digital circuits, but consumers continue to demand even more complex chip designs.

Today’s system-on-chip platforms incorporate billions of transistors with miles of electrical wiring that are integrated within a tiny chip. This technological feat requires large teams and complex software. As a result, the cost of circuit design continues to skyrocket, narrowing the competitive field to large, multinational companies capable of keeping up with the demand for capital and skilled talent. It’s becoming increasingly difficult for small entities, as well as the Department of Defense, to leverage the high-performance technology it needs to design complex circuits for defense applications.

“The high cost of this software creates a barrier to entry for smaller entities to compete in design efforts,” said Sachin Sapatnekar, a University of Minnesota professor of electrical and computer engineering who will lead the grant. “The goal of our research is to replace the proprietary model with an open-source software environment for analog and mixed-signal designs. In short, we seek to ‘democratize’ chip design by facilitating open access to chip design tools and seeding a
community of users. The result will be lower costs to consumers for electronics.”

Through the creation of a software-based, completely automated physical layout generator and an open-source intellectual property (IP) ecosystem, the IDEA program aims to create a “no human in the loop” layout generator that would enable users with limited electronic design expertise to complete the physical design of electronic hardware within 24 hours. The software created under IDEA would be capable of automatically creating circuit design files ready for manufacturing, reducing design time from months or years to a single day.

By applying machine learning methodologies, IDEA hopes to continuously evolve and improve the performance of the layout generator for digital circuits, mixed-signal integrated circuits, systems-in-package, and printed circuit boards.

“Through the IDEA program, DARPA aims to eliminate the Department of Defense’s resource and expertise gap associated with custom electronic hardware design for the most advanced technologies by enabling full automation and applying machine intelligence,” said Andreas Olofsson, the Microsystems Technology Office program manager leading IDEA. To read more about DARPA’s IDEA program and the newest round of funding, visit the DARPA website.

Congratulations to Eric Konitzer on the DEPS Scholarship

ECE graduate student Eric Konitzer is a recipient of the 2018-2019 Directed Energy Professional Society (DEPS) scholarship. Eric works with Prof. Joey Talghader in his optical MEMS group, investigating the next generation of infrared detectors for long wavelength infrared light. These sensors could eventually be used for very high precision thermal imaging. Specifically, Eric is examining how micro scale devices are prone to vibrations due to thermal energy. Although this effect is typically considered undesirable noise in MEMS systems, the optical MEMs group uses it to to their advantage as part of the design. Eric has been fabricating MEMS structures, and evaluating fabrication and measurement capabilities to learn what to expect for thermomechanical noise in some typical systems. Going forward, Eric will work on creating more complex structures that exploit materials’ properties in ways that can maximize an infrared detection signal. After graduation, he plans on working in related industry in research and development.

DEPS is the leading organization that facilitates and promotes communication on the development and application of directed energy (DE) (high energy lasers, and high power microwave systems and technologies). The Society supports research and development of directed energy technology for both defense and civil applications. Academic disciplines engaged in DE research include physics, electrical engineering, chemistry, chemical engineering, materials sciences, optical sciences, optical engineering, and aerospace engineering.

The deadline for the next round of scholarships (2019-2020) is April 12, 2019.