Vijay Janapa Reddi is an Associate Professor at Harvard University, Inference Co-chair for MLPerf, and a founding member of MLCommons, a nonprofit ML (Machine Learning) organization aiming to accelerate ML innovation. He also serves on the MLCommons board of directors. Before joining Harvard, he was an Associate Professor at The University of Texas at Austin in the Department of Electrical and Computer Engineering. His research interests include computer architecture and runtime systems, specifically in the context of autonomous machines and mobile and edge computing systems. Dr. Janapa Reddi is a recipient of multiple honors and awards, including the National Academy of Engineering (NAE) Gilbreth Lecturer Honor (2016), IEEE TCCA Young Computer Architect Award (2016), Intel Early Career Award (2013), Google Faculty Research Awards (2012, 2013, 2015, 2017, 2020), Best Paper at the 2020 Design Automation Conference (DAC), Best Paper at the 2005 International Symposium on Microarchitecture (MICRO), Best Paper at the 2009 International Symposium on High Performance Computer Architecture (HPCA), IEEE’s Top Picks in Computer Architecture awards (2006, 2010, 2011, 2016, 2017) and he has been inducted into the MICRO and HPCA Hall of Fame (in 2018 and 2019, respectively). He received a Ph.D. in computer science from Harvard University, M.S. from the University of Colorado at Boulder and B.S from Santa Clara University.
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Dive into the basics of machine learning applied to tiny devices with our Fundamentals of TinyML course. Gain a solid foundation in this emerging technology that's revolutionizing embedded systems such as wearables and IoT gadgets. Learn from experts and start your journey into the fascinating world of Tiny Machine Learning.
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Dive into the world of Tiny Machine Learning with our Applications of TinyML course. This hands-on program will guide you through real-world case studies, showcasing practical applications such as keyword spotting, visual wake words, and gesture recognition. Gain insights directly from industry experts on deploying models onto tiny or deeply embedded devices.
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Embark on the journey of building your own Tiny Machine Learning (TinyML) device. This course will guide you through programming in TensorFlow Lite for microcontrollers, enabling you to write code, deploy models, and ultimately create an entire TinyML application from scratch.
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Embark on a journey into Machine Learning Operations (MLOps) with a focus on scaling Tiny Machine Learning (TinyML). Learn best practices for deploying, monitoring, and maintaining ML models that are small enough to run on resource-constrained devices. This course is designed for developers, data scientists, and engineers who want to leverage the potential of tiny machine learning in their applications.