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<p class="MsoNormal"><span style="font-family:"Segoe UI",sans-serif;color:#201F1E;background:white">This is an announcement of Maksim <span class="markb8ja9ot38"><span style="border:none windowtext 1.0pt;padding:0in">Levental</span></span>'s MS Presentation</span><span style="font-family:"Segoe UI",sans-serif;color:#201F1E"><br>
<span style="background:white">===============================================</span><br>
<span style="background:white">Candidate: Maksim <span class="markb8ja9ot38"><span style="border:none windowtext 1.0pt;padding:0in">Levental</span></span></span><br>
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<span style="background:white">Date: Wednesday, February 09, 2022</span><br>
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<span style="background:white">Time: 3 pm CST</span><br>
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<span style="background:white">Remote Location: </span></span><a href="https://urldefense.com/v3/__https:/uchicago.zoom.us/j/2681314906?pwd=MFBwTURuYXJKa1QzcHp5UVdJbS9LQT09__;!!BpyFHLRN4TMTrA!rA-Rd5n_0W8sEBRfEqzbrdj9Wcv56LhyojTU9omqOz_HM6lfyMDSGfvqiD6vTDL-ttLiq48g$" target="_blank"><span style="font-family:"Segoe UI",sans-serif;border:none windowtext 1.0pt;padding:0in;background:white">https://uchicago.zoom.us/j/2681314906?pwd=MFBwTURuYXJKa1QzcHp5UVdJbS9LQT09</span></a><span style="font-family:"Segoe UI",sans-serif;color:#201F1E;background:white">
Meeting ID: 268 131 4906 Passcode: 123456</span><span style="font-family:"Segoe UI",sans-serif;color:#201F1E"><br>
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<span style="background:white">M.S. Paper Title: Memory Planning for Deep Neural Networks</span><br>
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<span style="background:white">Abstract: Deep neural networks (DNNs) are becoming increasingly memory intensive.</span><br>
<span style="background:white">We study memory allocation patterns in DNNs and propose a “memorization" based technique, \texttt{MemoMalloc}, for optimizing both memory usage and inference latency.</span><br>
<span style="background:white">Specifically, we use static memory planning techniques to reduce both peak memory consumption and heap mutex contention.</span><br>
<span style="background:white">We present an implementation of MemoMalloc and evaluate memory consumption and execution performance on a wide range of DNN architectures. MemoMalloc substantially outperforms state-of-the-art caching allocators in terms of execution
performance, by as much as 40%.</span><br>
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<span style="background:white">Advisors: Ian Foster</span><br>
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<span style="background:white">Committee Members: Ian Foster, Kyle Chard, and Raul Castro Fernandez</span></span><o:p></o:p></p>
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