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<div class="" style="word-wrap:break-word"><span class="" style="font-size:14.666666984558105px; font-family:"Segoe UI",sans-serif; color:rgb(32,31,30); background-color:white">This is an announcement of Maksim <span class="x_markb8ja9ot38"><span class="" style="border:1pt none windowtext; padding:0in">Levental</span></span>'s
MS Presentation</span><span class="" style="font-size:14.666666984558105px; font-family:"Segoe UI",sans-serif; color:rgb(32,31,30)"><br class="">
<span class="" style="background-color:white">===============================================</span><br class="">
<span class="" style="background-color:white">Candidate: Maksim <span class="x_markb8ja9ot38"><span class="" style="border:1pt none windowtext; padding:0in">Levental</span></span></span><br class="">
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<span class="" style="background-color:white">Date: Wednesday, February 09, 2022</span><br class="">
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<span class="" style="background-color:white">Time: 3 pm CST</span><br class="">
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<span class="" style="background-color: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" class="" style="color:rgb(5,99,193); font-family:Calibri,sans-serif; font-size:14.666666984558105px"><span class="" style="font-family:"Segoe UI",sans-serif; border:1pt none windowtext; padding:0in; background-color:white">https://uchicago.zoom.us/j/2681314906?pwd=MFBwTURuYXJKa1QzcHp5UVdJbS9LQT09</span></a><span class="" style="font-size:14.666666984558105px; font-family:"Segoe UI",sans-serif; color:rgb(32,31,30); background-color:white">
Meeting ID: 268 131 4906 Passcode: 123456</span><span class="" style="font-size:14.666666984558105px; font-family:"Segoe UI",sans-serif; color:rgb(32,31,30)"><br class="">
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<span class="" style="background-color:white">M.S. Paper Title: Memory Planning for Deep Neural Networks</span><br class="">
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<span class="" style="background-color:white">Abstract: Deep neural networks (DNNs) are becoming increasingly memory intensive.</span><br class="">
<span class="" style="background-color: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 class="">
<span class="" style="background-color:white">Specifically, we use static memory planning techniques to reduce both peak memory consumption and heap mutex contention.</span><br class="">
<span class="" style="background-color: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></span>
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<span class="" style="background-color:white">Advisors: Ian Foster</span><br class="">
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<span class="" style="background-color:white">Committee Members: Ian Foster, Kyle Chard, and Raul Castro Fernandez</span></span></div>
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