The talk entitled “When AI meets Performance Engineering: Challenges and Opportunities”, will be given by Prof. Lizy Kurian John (University of Texas at Austin) on 29 April, at 2.30 pm, in room L119 (DEMec). The session will be moderated by Prof. Pedro Diniz (DEI).
About the Talk:
“Artificial Intelligence/Machine Learning (AI/ML) has transformed hardware design, software systems, and system architectures. Emerging hardware for AI accelerators have become heterogeneous and complex, that traditional modeling methodologies, benchmarks, and metrics for prior systems may not work for assessing AI models and AI hardware. The single-most commonly used operation in AI is matrix multiplication. Hardware accelerators like GPUs have created Tensor Cores to support matrix multiplication. Google TPUs support matrix multiplication by the hardware systolic arrays. However, matrix multiplication cannot be a reliable benchmark for benchmarking, due to its high sensitivity to optimizations. The MLPerf consortium creates ML benchmarks, however those benchmarks are prohibitively difficult to manage for many small companies and academic researchers. What are good benchmarks for AI/ML? How can you evaluate AI hardware in pre-silicon and post-silicon stages? Complete system simulation was a popular mode of pre-silicon evaluation but prohibitively expensive. This talk will describe some of the opportunities and challenges when AI meets Performance Engineering.”
About the Speaker:
“Lizy Kurian John is Truchard Foundation Chair in Engineering at the University of Texas at Austin. Her research interests include workload characterization, performance evaluation, and high performance architectures for emerging workloads. She is recipient of many awards including Joe J. King Professional Engineering Achievement Award (2023), and The Pennsylvania State University Outstanding Engineering Alumnus Award (2011). She has authored 3 books and has edited 4 books including a book on Computer Performance Evaluation and Benchmarking. She holds 18 US patents and is an IEEE Fellow (Class of 2009), ACM Fellow, AAAS Fellow and Fellow of the National Academy of Inventors (NAI).”
Acknowledgement: Lizy K. John is currently a Fulbright Specialist. Her research is supported in part by the Unites States National Science Foundation (NSF) Grants #2326894, #2425655, Semiconductor Research Corporation (SRC) Task 3148.001, and NVIDIA Applied Research Accelerator Program Grant.