Presenter
Krishna Teja Chitty-Venkata

Biography
Krishna Teja Chitty-Venkata is a postdoctoral researcher in the AI/ML (previously Data Science) group of Argonne Leadership Computing Facility. He primarily works at the intersection of systems and deep learning. His research interests include neural network training, fine tuning and inference optimization on general purpose and AI accelerators, AI for science applications and HPC for AI. His recent work includes WActiGrad, a pruning method for LLMs, and LLM-Inference-Bench, a benchmarking suite for LLM Inference study.
Krishna finished his PhD in computer engineering at Iowa State University under the guidance of Professor Arun Somani. His dissertation research was primarily focused on developing pruning, quantization and neural architecture search algorithms for optimizing neural network inference on hardware accelerators, CPUs and GPUs. During the course of his PhD, he interned at AMD, Intel and Argonne National Laboratory, where he worked on several deep learning-related research problems. Earlier, he received his bachelor’s degree from the University College of Engineering, Osmania University, Hyderabad, India.
Krishna's research interests include: 1) hardware-aware inference optimization of deep neural networks; 2) enhancing training and fine tuning of large language models; 3) neural architecture search (AutoML); and 4) application of AI methods for science applications.
Krishna finished his PhD in computer engineering at Iowa State University under the guidance of Professor Arun Somani. His dissertation research was primarily focused on developing pruning, quantization and neural architecture search algorithms for optimizing neural network inference on hardware accelerators, CPUs and GPUs. During the course of his PhD, he interned at AMD, Intel and Argonne National Laboratory, where he worked on several deep learning-related research problems. Earlier, he received his bachelor’s degree from the University College of Engineering, Osmania University, Hyderabad, India.
Krishna's research interests include: 1) hardware-aware inference optimization of deep neural networks; 2) enhancing training and fine tuning of large language models; 3) neural architecture search (AutoML); and 4) application of AI methods for science applications.
Presentations
Workshop
Accelerators
Modeling and Simulation
Performance Evaluation and/or Optimization Tools
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