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    <title>Olivia Weng</title>
    <link>http://oliviaweng.github.io/</link>
    <description>Recent content on Olivia Weng</description>
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    <item>
      <title>On taking walks after lunch</title>
      <link>http://oliviaweng.github.io/blog/lunch-walks/</link>
      <pubDate>Fri, 29 Nov 2019 16:57:20 -0500</pubDate>
      
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      <description>Dear reader, Taking walks after lunch, mostly alone but occasionally with friends, has been one of the best decisions I&amp;rsquo;ve made in college. I used to approach college with a keep-your-foot-on-the-gas-for-the-love-of-all-that-is-good mentality. Not working was bad and a waste of time. Then the unsustainability of it all hit me around my third year. It was only halfway through the year, and I woke up every day feeling burned out, worn out.</description>
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    <item>
      <title>About</title>
      <link>http://oliviaweng.github.io/about/</link>
      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
      
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      <description>Hello! I&amp;rsquo;m Olivia. I&amp;rsquo;m a fifth-year PhD candidate in Computer Science working with Professor Ryan Kastner at UC San Diego. My research focuses on using hardware-software co-design to create efficient, fault-tolerant computer architectures for machine learning. My work is supported by an NSF Graduate Research Fellowship.
Before coming to UCSD, I received my BS in Computer Science at the University of Chicago, where I worked with Professor Yanjing Li on mitigating cache side-channel attacks while maintaining cache performance and Professor Andrew A.</description>
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      <title>Projects</title>
      <link>http://oliviaweng.github.io/projects/</link>
      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
      
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      <description>AmigoLUT: Scaling Up LUT-based Neural Networks with Ensemble Learning Applications including high-energy physics and cybersecurity require extremely high throughput and low latency neural network inference on FPGAs. Lookup Table (LUT)-based NNs like LogicNets address these constraints by mapping neural networks directly to LUTs, achieving inference latency on the order of nanoseconds. However, it is difficult to implement larger, more performant LUT-based NNs because LUT resource usage increases exponentially with respect to the number of LUT inputs.</description>
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