Cong Li

Employment

• Intel Corporation Principal Engineer
  2003 - Present
• Microsoft Research Asia (China) Assistant Researcher
  2001 - 2003

Education

Education history is unavailable.

Projects & Funding

Projects & funding information is unavailable.

Publications (25)

• Foreseer: Knowledge-Driven Acceleration of Memory-Bound Matrix Multiplications for Large Language Model Inference
  2024 DOI: 10.1145/3688351.3689153
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• From Correctable Memory Errors to Uncorrectable Memory Errors: What Error Bits Tell
  SC22: International Conference for High Performance Computing, Networking, Storage and Analysis 2022 DOI: 10.1109/sc41404.2022.00081
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• SHARC: improving adaptive replacement cache with shadow recency cache management
  Proceedings of the 22nd International Middleware Conference 2021 DOI: 10.1145/3464298.3493389
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• Fault-Aware Prediction-Guided Page Offlining for Uncorrectable Memory Error Prevention
  2021 IEEE 39th International Conference on Computer Design (ICCD) 2021 DOI: 10.1109/iccd53106.2021.00077
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• Predicting Uncorrectable Memory Errors from the Correctable Error History: No Free Predictors in the Field
  The International Symposium on Memory Systems 2021 DOI: 10.1145/3488423.3519316
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• CPI for Runtime Performance Measurement: The Good, the Bad, and the Ugly
  2020 IEEE International Symposium on Workload Characterization (IISWC) 2020 DOI: 10.1109/iiswc50251.2020.00019
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• DPCLS: Improving Partial Cache Line Sparing with Dynamics for Memory Error Prevention
  2020 IEEE 38th International Conference on Computer Design (ICCD) 2020 DOI: 10.1109/iccd50377.2020.00045
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• Runtime Estimation of Application Memory Latency for Performance Analysis and Optimization
  The International Symposium on Memory Systems 2020 DOI: 10.1145/3422575.3422773
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• Predicting Uncorrectable Memory Errors for Proactive Replacement: An Empirical Study on Large-Scale Field Data
  2020 16th European Dependable Computing Conference (EDCC) 2020 DOI: 10.1109/edcc51268.2020.00016
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• Combining global regression and local approximation in server power modeling
  SICS Software-Intensive Cyber-Physical Systems 2019 DOI: 10.1007/s00450-018-0391-x
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