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<p class="MsoNormal"><span style="color:black">New Course Offering:<o:p></o:p></span></p>
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<p class="MsoNormal"><b><u><span style="color:black">EE 6900: Hardware for Deep Learning (Monday, Wednesday, Friday from 12:55 PM - 1:50 PM in ARC 101)</span> 3 credit hours</u></b><span style="color:black"><o:p></o:p></span></p>
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<p class="MsoNormal"><span class="contentpasted0"><span style="font-size:10.5pt;font-family:"Tahoma",sans-serif;color:black">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.</span></span><span style="color:black"><o:p></o:p></span></p>
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<p class="MsoNormal"><span class="contentpasted0"><span style="font-size:10.5pt;font-family:"Tahoma",sans-serif;color:black">Thanks</span></span><span style="color:black"><o:p></o:p></span></p>
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<p class="MsoNormal"><span class="contentpasted0"><span style="font-size:10.5pt;font-family:"Tahoma",sans-serif;color:black">Avinash.</span></span><span style="color:black"><o:p></o:p></span></p>
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<p style="background:white"><span style="font-size:12.0pt;color:#999999">------------------------------------------------------------------------</span><span style="font-size:12.0pt;color:black"><o:p></o:p></span></p>
<p style="background:white;text-align:start"><span style="font-size:10.0pt;font-family:"Georgia",serif;color:gray">Avinash Karanth</span><span style="color:#201F1E"><o:p></o:p></span></p>
<p style="background:white;text-align:start"><span style="font-size:10.0pt;font-family:"Georgia",serif;color:gray">Director & Chair, School of Electrical Engineering and Computer Science</span><span style="color:#201F1E"><o:p></o:p></span></p>
<p style="background:white;text-align:start"><span style="font-size:10.0pt;font-family:"Georgia",serif;color:gray">Joseph K. Jachinowski Professor in EECS</span><span style="color:#201F1E"><o:p></o:p></span></p>
<p style="background:white;text-align:start"><span style="font-size:10.0pt;font-family:"Georgia",serif;color:gray">Associate Editor – IEEE Transactions on Computers</span><span style="color:#201F1E"><o:p></o:p></span></p>
<p style="background:white;text-align:start"><span style="font-size:10.0pt;font-family:"Georgia",serif;color:gray">Associate Editor – IEEE Transactions on Cloud Computing</span><span style="color:#201F1E"><o:p></o:p></span></p>
<p style="background:white;text-align:start"><span style="font-size:10.0pt;font-family:"Georgia",serif;color:gray">Ohio University, Athens, OH 45701.</span><span style="color:#201F1E"><o:p></o:p></span></p>
<p style="background:white;text-align:start"><span style="font-size:10.0pt;font-family:"Georgia",serif;color:gray">Phone: 740-597-1481</span><span style="color:#201F1E"><o:p></o:p></span></p>
<p style="background:white;text-align:start"><span style="font-size:10.0pt;font-family:"Georgia",serif;color:gray">Webpage:
<a href="https://oucsace.cs.ohio.edu/~avinashk">https://oucsace.cs.ohio.edu/~avinashk</a></span><span style="color:#201F1E"><o:p></o:p></span></p>
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