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Guo, Xinfei
University of Virginia
Charlottesville, Virginia
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Xinfei Guo is a final-year PhD candidate in the Computer Engineering Department at the University of Virginia (UVA), Charlottesville, VA. He received his BS degree in Electronic Materials and Devices in 2010 from Xidian University, China and his MSEE degree in 2012 from the University of Florida, Gainesville, FL. He joined the High Performance Low Power (HPLP) Lab at UVA in 2012, and began his PhD career. He has a broad interest in digital circuits and microarchitectures and has been working on power and reliability aspects. His current research projects focus on aging and accelerated recovery techniques (BTI and EM), cross-layer resilience design methodology, and energy-efficient design. Most recently, he started working in the Internet of Things (IoT) security and reliability field, including novel specialized architectures. During his PhD work, he has collaborated closely with Intel, AMD, IBM, and many other research groups, and publishes frequently at multiple refereed conferences and workshops, and in journals in the VLSI and computer architecture fields.

He has been a committed reviewer for Computing Reviews since 2015, and has served as a reviewer for multiple IEEE and ACM journals (such as TCAD and TCAS-I) and conferences (such as DAC, ISCAS, ICCAD, and ISVLSI). He is a student member of the IEEE and ACM, and received the A. Richard Newton Young Student Fellowship from the 50th Design Automation Conference and the Achievement Award from the University of Florida in 2013 and 2010.

For more details, please visit his LinkedIn profile and personal website.

Date Reviewed  
- 10 of 10 reviews

  A taxonomy of job scheduling on distributed computing systems
Lopes R., Menasce D.  IEEE Transactions on Parallel and Distributed Systems 27(12): 3412-3428, 2016. Type: Article

In the field of computing theory or computer science in general, there are many problems that are computationally hard. These problems attract many researchers who try to solve them. But one very important aspect needs to be done before solving th...

Mar 30 2017  
  Frame buffer-less stream processor for accurate real-time interest point detection
Licciardo G., Boesch T., Pau D., Di Benedetto L.  Integration, the VLSI Journal 54(C): 10-23, 2016. Type: Article

In the big data era, pictures are used as key information points in various applications such as ecommerce, entertainment, social networking, and medical diagnostics. Images contain huge amounts of information, and processing an image requires man...

Nov 16 2016  
   Area-energy tradeoffs of logic wear-leveling for BTI-induced aging
Ashraf R., Khoshavi N., Alzahrani A., DeMara R., Kiamehr S., Tahoori M.  CF 2016 (Proceedings of the ACM International Conference on Computing Frontiers, Como, Italy,  May 16-19, 2016) 37-44, 2016. Type: Proceedings

As technology nodes are scaled down into the nanometer level, the reliability of the devices for an electronic chip becomes critical. Among various reliability threats, aging is one of the most prominent, as it introduces gradual parameter and met...

Oct 4 2016  
   RT level timing modeling for aging prediction
Koppaetzky N., Metzdorf M., Eilers R., Helms D., Nebel W.  DATE 2016 (Proceedings of the 2016 Conference on Design, Automation & Test in Europe, Dresden, Germany,  Mar 14-18, 2016) 297-300, 2016. Type: Proceedings

Transistor aging has grown as a critical reliability issue that shortens the lifetime of electronic devices and slows down the circuits inside these devices. During the past decade, researchers have proposed various techniques to deal with aging-r...

Sep 9 2016  
  Fine-grained appliance usage and energy monitoring through mobile and power-line sensing
Roy N., Pathak N., Misra A.  Pervasive and Mobile Computing 30132-150, 2016. Type: Article

Energy consumption is a current concern in many aspects of daily life. The concept of smart buildings, where researchers propose solutions to tracking energy consumption by installing sensors and conducting smart analysis to predict and/or prevent...

Aug 18 2016  
   Reliability-aware design to suppress aging
Amrouch H., Khaleghi B., Gerstlauerz A., Henkel J.  DAC 2016 (Proceedings of the 53rd Annual Design Automation Conference, Austin, TX,  Jun 5-9, 2016) 1-6, 2016. Type: Proceedings

In the biological world, humans age over time. Similar to this, small components like transistors in an electronic chip also age as the system runs. This will slow the system down and cause potential failures while the system is still running. Eve...

Jun 28 2016  
   Modeling, profiling, and debugging the energy consumption of mobile devices
Hoque M., Siekkinen M., Khan K., Xiao Y., Tarkoma S.  ACM Computing Surveys 48(3): 1-40, 2015. Type: Article

Because mobile devices, like smartphones, are usually powered with small-size batteries that have limited capacity and life, hardware engineers need to be smart at designing energy-efficient systems to run the applications with minimal energy. On ...

Jun 23 2016  
   Beat frequency detector-based high-speed true random number generators: statistical modeling and analysis
Lao Y., Tang Q., Kim C., Parhi K.  ACM Journal on Emerging Technologies in Computing Systems 13(1): 1-25, 2016. Type: Article

With the increased number of personal electronic devices, the security of these devices and personal information has become a big concern. As effective solutions to securing a system, encryption and the signing of confidential information with dig...

Jun 16 2016  
  A MapReduce scratchpad memory for multi-core cloud computing applications
Kachris C., Sirakoulis G., Soudris D.  Microprocessors & Microsystems 39(8): 599-608, 2015. Type: Article

MapReduce is a programming framework that is widely used for data center cloud computing or multicore system-on-chip (SoC) applications. The goal is to process and generate large datasets. MapReduce is a two-step function: the first step, called M...

May 19 2016  
   Delay/power modeling and optimization of FinFET circuit modules under PVT variations: observing the trends between the 22nm and 14nm technology nodes
Tang A., Gao X., Chen L., Jha N.  ACM Journal on Emerging Technologies in Computing Systems 12(4): 1-21, 2016. Type: Article

Driven by the increasing demands of low power and high performance, the semiconductor industry has pushed the device down to below 20 nanometer (nm) scale. Downscaling introduced several challenges, among which short channel effect is one of the m...

Apr 28 2016  
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