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<p class="MsoNormal"><span style="color:#212121;mso-ligatures:none">This is an announcement of Maksim Levental's Candidacy Exam.<o:p></o:p></span></p>
<p class="MsoNormal"><span style="color:#212121;mso-ligatures:none">===============================================<o:p></o:p></span></p>
<p class="MsoNormal"><span style="color:#212121;mso-ligatures:none">Candidate: Maksim Levental<o:p></o:p></span></p>
<p class="MsoNormal"><span style="color:#212121;mso-ligatures:none"> <o:p></o:p></span></p>
<p class="MsoNormal"><span style="color:#212121;mso-ligatures:none">Date: October 23,2023<o:p></o:p></span></p>
<p class="MsoNormal"><span style="color:#212121;mso-ligatures:none"> <o:p></o:p></span></p>
<p class="MsoNormal"><span style="color:#212121;mso-ligatures:none">Time: 2:00pm-3:00pm<o:p></o:p></span></p>
<p class="MsoNormal"><span style="color:#212121;mso-ligatures:none"> <o:p></o:p></span></p>
<p class="MsoNormal"><span style="color:#212121;mso-ligatures:none">Remote Location: <o:p></o:p></span></p>
<p class="MsoNormal"><span style="color:#212121;mso-ligatures:none"> <o:p></o:p></span></p>
<p class="MsoNormal"><span style="color:#212121;mso-ligatures:none">Location: JCL 298<o:p></o:p></span></p>
<p class="MsoNormal"><span style="color:#212121;mso-ligatures:none"> <o:p></o:p></span></p>
<p class="MsoNormal"><span style="color:#212121;mso-ligatures:none">Title: TOWARDS AN ABSTRACTION-FREE PROGRAMMING MODEL FOR DEEP<o:p></o:p></span></p>
<p class="MsoNormal"><span style="color:#212121;mso-ligatures:none">LEARNING ACCELERATORS<o:p></o:p></span></p>
<p class="MsoNormal"><span style="color:#212121;mso-ligatures:none"> <o:p></o:p></span></p>
<p class="MsoNormal"><span style="color:#212121;mso-ligatures:none">Abstract: The thesis of this dissertation is that it is possible to design a programming model for expert ML developers to both represent Deep Neural Networks simultaneously at multiple levels
of abstraction and generate optimal, target-specific, code for novel accelerator architectures. Specifically, that by leveraging and extending an existing compiler, we can provide both an embedded domain specific language, including tensor, vector, scalar
operations, concurrency, memory hierarchy, explicit data movement, and we can perform optimal code generation for novel architectures. This thesis is derived of experience over the course of several projects (see Chapter 1), each of which involved manipulating
highly abstract APIs and struggling to achieve runtime performance. We validate this thesis by leveraging and extending MLIR to provide both an embedded domain specific language and optimal code generation for AMD XDNA spatial architectures. We evaluate our
framework by designing, optimizing, and benchmarking several Large Language Models.<o:p></o:p></span></p>
<p class="MsoNormal"><span style="color:#212121;mso-ligatures:none"> <o:p></o:p></span></p>
<p class="MsoNormal"><span style="color:#212121;mso-ligatures:none">Advisors: Ian Foster<o:p></o:p></span></p>
<p class="MsoNormal"><span style="color:#212121;mso-ligatures:none"> <o:p></o:p></span></p>
<p class="MsoNormal"><span style="color:#212121;mso-ligatures:none">Committee Members: Ian Foster, Kyle Chard ,Raul Fernandez, & Stephen Neuendorffer<o:p></o:p></span></p>
<p class="MsoNormal"><span style="color:#212121;mso-ligatures:none"> <o:p></o:p></span></p>
<p class="MsoNormal"><span style="color:#212121;mso-ligatures:none"> <o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:9.0pt;font-family:Helvetica;color:#212121;mso-ligatures:none">_______________________________________________</span><span style="color:#212121;mso-ligatures:none"><o:p></o:p></span></p>
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<p class="MsoNormal"><o:p> </o:p></p>
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