New Course Offering: EE 6900: Hardware for Deep Learning (Monday, Wednesday, Friday from 12:55 PM - 1:50 PM in ARC 101) This course is intended to provide graduate students with an in-depth study of the underlying hardware needed for deep learning and machine learning applications. As the neural network model size and complexity increases for improved accuracy, computational complexity and energy consumption increases proportionally. In this course, students will understand deep network computations for vision and image processing applications using hardware accelerators. Techniques to reduce the computation burden such as quantization, optimized dataflow and mapping, pruning and compression will also be discussed in this course. As data movement plays a crucial role in hardware mapping and optimizations, the design of interconnects for hardware accelerators for various neural network models such as CNN, LSTM, RNN, transformer and attention models will be discussed. Thanks Avinash. ------------------------------------------------------------------------ Avinash Karanth Director & Chair, School of Electrical Engineering and Computer Science Joseph K. Jachinowski Professor in EECS Associate Editor - IEEE Transactions on Computers Associate Editor - IEEE Transactions on Cloud Computing Ohio University, Athens, OH 45701. Phone: 740-597-1481 Webpage: https://oucsace.cs.ohio.edu/~avinashk -------------- next part -------------- An HTML attachment was scrubbed... URL: < http://listserv.ohio.edu/pipermail/eecs_mscs/attachments/20230825/b89fd8dd/attachment.html >
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