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<div class="PlainText">This is an announcement of Maksim Levental's MS Presentation<br>
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Candidate: Maksim Levental<br>
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Date: Wednesday, February 09, 2022<br>
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Time: 3 pm CST<br>
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Remote Location: <a href="https://uchicago.zoom.us/j/2681314906?pwd=MFBwTURuYXJKa1QzcHp5UVdJbS9LQT09">
https://uchicago.zoom.us/j/2681314906?pwd=MFBwTURuYXJKa1QzcHp5UVdJbS9LQT09</a> Meeting ID: 268 131 4906 Passcode: 123456<br>
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M.S. Paper Title: Memory Planning for Deep Neural Networks<br>
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Abstract: Deep neural networks (DNNs) are becoming increasingly memory intensive.<br>
We study memory allocation patterns in DNNs and propose a “memorization" based technique, \texttt{MemoMalloc}, for optimizing both memory usage and inference latency.<br>
Specifically, we use static memory planning techniques to reduce both peak memory consumption and heap mutex contention.<br>
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%.<br>
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Advisors: Ian Foster<br>
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Committee Members: Ian Foster, Kyle Chard, and Raul Castro Fernandez<br>
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