Computer Science and Engineering
School of Computing, Informatics, and Decision Systems Engineering
Honors Faculty, Arizona State University
Ph.D., Princeton University
M.A., Princeton University
B.S., Cornell University
- Software and system stack design and optimization for domain-specific workloads
- High-performance and energy-efficient heterogeneous architecture
- Performance quality modeling and energy efficiency optimization for mobile
- Energy harvesting for emerging computing devices
- Energy management for portable electronics
- Temperature-aware computing
- Advanced processor and system cooling designs and management
- Performance characterization, analysis and prediction
- High-performance and power-efficient memory designs
Carole-Jean Wu is an Associate Professor of Computer Science and Engineering in Arizona State University. She holds a Research Scientist position with Facebook’s AI Infrastructure Research. She is a senior member of both ACM and IEEE.
Prof. Wu works in the area of Computer and System Architectures. In particular, her research focuses on high-performance and energy-efficient computer architecture through hardware heterogeneity, energy harvesting techniques for emerging computing devices, temperature and energy management for portable electronics. More recently, her research has pivoted into designing systems for machine learning. She is the leading author of “Machine Learning at Facebook: Understanding Inference at the Edge” which presents unique design challenges faced when deploying machine learning solutions at scale to the edge, from over billions of smartphones in the wild to Facebook’s virtual reality platforms. She is the recipient of the 2018 IEEE ITHERM Best Paper Award, the 2017 NSF CAREER Award, the 2017 IEEE Young Engineer of the Year Award, the 2014 IEEE Best of Computer Architecture Letter Award, the 2013 Science Foundation Arizona Bisgrove Early Career Scholarship, and the 2011-12 Intel Ph.D. Fellowship. Her research has been supported by both industry sources and the National Science Foundation.
She currently serves on the Steering Committee of the IEEE International Symposium on Performance Analysis of Systems and Software and is the Program Chair for the 2018 IEEE International Symposium on Workload Characterization. She is also co-chairing the MLPerf Edge Inference WG. She served on the Executive Committee of the IEEE Technical Committee on Computer Architecture (TCCA) 2017-18. Prof. Wu received her M.A. and Ph.D. degrees in Electrical Engineering from Princeton University. She completed a B.Sc. degree in Electrical and Computer Engineering from Cornell University.
- C.-J. Wu et al., “Machine Learning at Facebook: Understanding Inference at the Edge,” HPCA-2019.
- A. Arunkumar, E. Bolotin, D. Nellans, and C.-J. Wu, “Understanding the Future of Energy Efficiency in Multi-Module GPUs,” HPCA-2019.
- A. Arunkumar, S.-Y. Lee, V. Soundararajan, and C.-J. Wu, “LATTE-CC: Latency Tolerance Aware Adaptive Cache Compression Management for Energy Efficient GPUs,” HPCA-2018.
- A. Arunkumar et al., “MCM-GPU: Multi-Chip-Module GPUs for Continued Performance Scalability,” ISCA-2017.
- B. Gaudette, C.-J. Wu, and S. Vrudhula, “Improving Smartphone/Mobile User Experience by Balancing Performance and Energy with Probabilistic QoS Guarantee,” HPCA-2016.
- S.-Y. Lee, A. Arunkumar, and C.-J. Wu, “CAWA: Coordinated Warp Scheduling and Cache Prioritization for Critical Warp Acceleration for GPGPU Workloads,” ISCA-2015.
- S.-Y. Lee and C.-J. Wu, “CAWS: Criticality-Aware Warp Scheduling for GPGPU Workloads,” PACT-2014.
- C.-J. Wu, A. Jaleel, M. Martonosi, S. Steely Jr., and J. Emer, “PACMan: Prefetch-Aware Cache Management for High Performance Caching,” MICRO-2011.
- C.-J. Wu, A. Jaleel, W. Hasenplaugh, M. Martonosi, S. Steely Jr., and J. Emer, “SHiP: Signature-Based Hit Predictor for High Performance Caching,” MICRO-2011.
Curriculum Vitae (Updated on November 15, 2018)
- Jhe-Yu Liou (PhD candidate; 2015 — present)
- Srinath Dasari (PhD candidate; 2016 — present)
- Akhil Arunkumar (PhD 2018) [First employment: Samsung Austin R&D Center] [Memory Subsystem Optimization Techniques for Modern High-Performance General-Purpose Processors]
- Viraj Wadhwa (High school intern from BASIS Chandler Primary, 2017-18. Now an undergraduate student at UT-Austin) [Improving Image Recognition with Tensor Flow API for Autonomous Driving]
- TJ Smith (Research Experience for Undergraduates (REU) from Princeton EE; 2017)
- Katherine Hann (High school intern from Xavier College Preparatory High School, 2017. Now an undergraduate student at University of Pennsylvania) [Designing A Paired Robotic Car Indoor Navigation and Tracking System]
- Rashmi Athavale (High school intern from Hamilton High School, 2017. Now an undergraduate student at Georgia Tech) [Designing A Paired Robotic Car Indoor Navigation and Tracking System]
- Benjamin Gaudette (PhD 2017; co-advised with Prof. Sarma Vrudhula) [First employment: Benchmark Electronics] [An Intelligent Framework for Energy-aware Mobile Computing Subject to Stochastic System Dynamics]
- Shin-Ying Lee (PhD 2017) [First employment: Samsung Austin R&D Center] [Intelligent Scheduling and Memory Management Techniques For Modern GPU Architectures]
- Received the Outstanding Computer Engineering PhD Graduate Student Award
- Kody Stribrny (BS 2017; co-advised with Prof. Sarma Vrudhula) [First employment: Amazon] [Honors Thesis: Mobile Waterway Monitor]
- Davesh Shingari (MS 2016) [First employment: Marvell] [Memory Interference Characterization and Mitigation for Heterogeneous Smartphones]
- Soochan Lee (PhD 2015; co-advised with Prof. Patrick E. Phelan) [First employment: LG Electronics] [A Study of Latent Heat of Vaporization in Aqueous Nanofluids]
- Ryan Brazens (BS 2014) [First employment: Intel]
- Dhinakaran Pandiyan (MS 2014) [First employment: Intel] [Data Movement Energy Characterization of Emerging Smartphone Workloads for Mobile Platforms]
- Received the Outstanding Computer Engineering MS Graduate Student Award
- Amrit Panda (PhD 2014; co-advised with Prof. Karam S. Chatha) [First employment: Qualcomm Research] [StreamWorks: An Energy-efficient Embedded Co-processor for Stream Computing]
- Ying-Ju Yu (2016-17) [First employment: Intel]
Note to New Students
- If you are a prospective student, please read this note before contacting me about graduate school. I am a faculty member in Computer Science and Engineering in the School of Computing, Informatics, and Decision Systems Engineering of Arizona State University. The department offers graduate degrees in Computer Science as well as Computer Engineering. Therefore, if you are interested in ASU, I encourage you to apply to the degree major that best suits your background and interests.
If you are a student at ASU and are interested in my research work, I encourage you to take my courses (Computer Architecture and/or Advanced Computer Architecture) and perform well in class. In general, I am happy to talk to students who show a sincere interest in understanding my research before expressing a generic interest in working with me.
If you are considering to apply for PhD programs, here is the document “Applying to Ph.D. Programs in Computer Science” written by Mor Harchol-Balter. Reading her document would be very instructive. I am also happy to discuss about graduate school opportunities with students who consider this path.