Recent Papers

  1. Weilong Cui, Yongshan Ding, Deeksha Dangwal, Adam Holmes, Joseph McMahan, Ali Javadi-Abhari, Georgios Tzimpragos, Frederic T. Chong, and Timothy Sherwood. Charm: A Language for Closed-form High-level Architecture Modeling, In the proceedings of the 45th Annual Intl. Symposium on Computer Architecture (ISCA), June 2018. Los Angeles, California.
  2. Ali Javadi-Abhari, Pranav Gokhale, Adam Holmes, Diana Franklin, Ken Brown, Margaret Martonosi, and Frederic T. Chong. Optimized Surface Code Communication in Superconducting Quantum Computers, International Symposium on Microarchitecture. November 2017. Boston, MA.
  3. Zhaoxia Deng, Lunkai Zhang, Nikita Mishra, Henry Hoffman and Frederic T. Chong. Memory Cocktail Therapy: A General Learning-Based Framework to Optimize Dynamic Tradeoffs in NVMs, International Symposium on Microarchitecture. November 2017. Boston, MA.
  4. Frederic T. Chong, Diana Franklin, and Margaret Martonosi. Programming Languages and Compiler Design for Realistic Quantum Hardware , Nature. September 2017.
  5. Zhaoxia Deng, Ariel Feldman, Stuart A. Kurtz, and Frederic T. Chong. Lemonade from Lemons: Harnessing Device Wearout to Create Limited-Use Security Architectures, In the proceedings of the 44rd Annual Intl. Symposium on Computer Architecture (ISCA) , June 2017. Toronto, Canada.


Fred Chong is the Seymour Goodman Professor in the Department of Computer Science at the University of Chicago. He is also Lead Principal Investigator for the EPiQC Project (Enabling Practical-scale Quantum Computing), an NSF Expedition in Computing. Chong received his Ph.D. from MIT in 1996 and was a faculty member and Chancellor's fellow at UC Davis from 1997-2005. He was also a Professor of Computer Science, Director of Computer Engineering, and Director of the Greenscale Center for Energy-Efficient Computing at UCSB from 2005-2015. He is a recipient of the NSF CAREER award and 6 best paper awards. His research interests include emerging technologies for computing, quantum computing, multicore and embedded architectures, computer security, and sustainable computing. Prof. Chong has been funded by NSF, Intel, Google, AFOSR, IARPA, DARPA, Mitsubishi, Altera and Xilinx. He has led or co-led over $31M in awarded research, and been co-PI on an additional $10M.


Quantum Computing
Quantum computing is at the cusp of a computing revolution. A physical machine with 100 quantum bits is expected in the next 3-5 years, large enough to solve problems that classical machines can not solve. Our work focuses on the near- and long-term design of the algorithms, software and architectures of scalable quantum computing systems. More.
Hardware Support for Trustworthy Computing
Privacy and integrity are important security concerns. These concerns are addressed by controlling information flow, i.e., restricting how information can flow through a system. We have designed intrusion-resistant architectures, architectures with provable information separation properties, as well as a hardware-description language and compiler. Our most recent work will explore the interaction between error-resilience and security, leveraging information flow analysis to more efficiently enforce resilience and security properties. More.
Architectures for New Memory Technologies
New memory technologies have potential advantages of non-volatility, high density, multi-bit storage, and support for simple computations. Unfortunately, they also have disadvantages in terms of wearout, long write latencies, and high write energies. Our research explores system designs that exploit these advantages and mitigate these disadvantages. More.
Statistical Program Analysis
Program behavior can be easily modeled when program outputs are simple and continuous across input ranges. Real programs, however, exhibit complex and discontinuous behavior. Our research seeks to leverage analyses of program structure to aid in the statistical analysis of program behavior. Our techniques could have significant implications for computational sampling, software testing, and information flow analysis.
Architectures for Data and Computation Similarity
Multi-threaded and parallel programs exhibit a surprising amount of computation and data similarity across tasks. Our research explores architectures that efficiently exploit these similarities. More.