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<div class="PlainText">This is an announcement of Chengcheng Wan's Dissertation Defense.<br>
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Candidate: Chengcheng Wan<br>
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Date: Friday, April 08, 2022<br>
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Time: 1:30 pm CST<br>
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Remote Location: <a href="https://zoom.us/j/93204773188?pwd=Qk54QUttYVZ0QnI4dHFGQnp3RzZoQT09">
https://zoom.us/j/93204773188?pwd=Qk54QUttYVZ0QnI4dHFGQnp3RzZoQT09</a> Meeting ID: 932 0477 3188 Passcode: D70JDv<br>
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Title: Correctness, Performance, And Energy-Efficiency: Improving Software Systems That Use Machine Learning Components<br>
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Abstract: Machine learning (ML) provides efficient solutions for a number of problems that are difficult to solve with traditional computing techniques. Deep neural networks (DNNs) have become a key workload for many computing systems due to their high inference
accuracy. This accuracy, however, comes at a cost of long latency, high energy usage, and engineering effort. It requires tremendous human effort and domain knowledge to implement a correct, efficient, and robust ML software.<br>
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To improve the correctness, performance, and energy-efficiency of machine learning software systems, this dissertation works on these three parts and makes the following contributions:<br>
First, to improve the flexibility of neural networks, this dissertation proposes a novel neural network architecture and a customized optimizer that support anytime prediction. This design allows one neural network to generate a series of increasingly accurate
outputs over time without sacrificing accuracy for flexibility.<br>
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Second, this dissertation designs a runtime scheduler ALERT, which holistically configures neural networks and system resources together to meet application-specific accuracy, performance, and energy-consumption constraints. It uses a probabilistic model to
detect environmental volatility and makes use of the full potential of the DNN candidate set to optimize performance and satisfy constraints.<br>
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Third, in the scope of software applications, this dissertation conducts the first comprehensive study about how real-world applications are using machine learning cloud APIs. We generalize 8 anti-patterns that degrade functional, performance, or economical
quality of the software. Guided by this study, we propose Keeper, a new testing framework for software systems that use machine learning APIs. Keeper automatically generates many test cases to thoroughly test every branch in the specified function and its
callees. It analyzes the test runs and reports many failures, as well as potential patches, to developers.<br>
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Advisors: Shan Lu<br>
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Committee Members: Shan Lu, Hank Hoffmann, and Michael Maire<br>
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