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<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Eric X. Liu's Personal Page</title><link>https://ericxliu.me/</link><description>Recent content on Eric X. Liu's Personal Page</description><generator>Hugo</generator><language>en</language><lastBuildDate>Fri, 19 Dec 2025 23:02:31 -0800</lastBuildDate><atom:link href="https://ericxliu.me/index.xml" rel="self" type="application/rss+xml"/><item><title>About</title><link>https://ericxliu.me/about/</link><pubDate>Fri, 19 Dec 2025 22:46:12 -0800</pubDate><guid>https://ericxliu.me/about/</guid><description>&lt;p&gt;Hi, I&amp;rsquo;m &lt;strong&gt;Eric Liu&lt;/strong&gt;.&lt;/p&gt;
<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Eric X. Liu's Personal Page</title><link>https://ericxliu.me/</link><description>Recent content on Eric X. Liu's Personal Page</description><generator>Hugo</generator><language>en</language><lastBuildDate>Sat, 20 Dec 2025 09:52:07 -0800</lastBuildDate><atom:link href="https://ericxliu.me/index.xml" rel="self" type="application/rss+xml"/><item><title>About</title><link>https://ericxliu.me/about/</link><pubDate>Fri, 19 Dec 2025 22:46:12 -0800</pubDate><guid>https://ericxliu.me/about/</guid><description>&lt;img src="https://ericxliu.me/images/about.jpeg" alt="Eric Liu" width="300" style="float: left; margin-right: 1.5rem; margin-bottom: 1rem; border-radius: 8px;"/&gt;
&lt;p&gt;Hi, I&amp;rsquo;m &lt;strong&gt;Eric Liu&lt;/strong&gt;.&lt;/p&gt;
&lt;p&gt;I am a &lt;strong&gt;Staff Software Engineer and Tech Lead Manager (TLM)&lt;/strong&gt; at &lt;strong&gt;Google&lt;/strong&gt;, based in Sunnyvale, CA.&lt;/p&gt;
&lt;p&gt;My work focuses on &lt;strong&gt;Platforms Performance and Customer Engineering&lt;/strong&gt;, specifically for &lt;strong&gt;GPUs and TPUs&lt;/strong&gt;. I lead teams that bridge the gap between cutting-edge AI hardware and the latest ML models (like Gemini), ensuring optimal performance and reliability at Google Cloud scale.&lt;/p&gt;
&lt;p&gt;Beyond the code, I maintain this &amp;ldquo;digital garden&amp;rdquo; where I document my projects and learnings. It serves as my second brain, capturing everything from technical deep dives to random musings.&lt;/p&gt;</description></item><item><title>The Convergence of Fast Weights, Linear Attention, and State Space Models</title><link>https://ericxliu.me/posts/the-convergence-of-fast-weights-linear-attention-and-state-space-models/</link><pubDate>Fri, 19 Dec 2025 00:00:00 +0000</pubDate><guid>https://ericxliu.me/posts/the-convergence-of-fast-weights-linear-attention-and-state-space-models/</guid><description>&lt;p&gt;Modern Large Language Models (LLMs) are dominated by the Transformer architecture. However, as context windows grow, the computational cost of the Transformer’s attention mechanism has become a primary bottleneck. Recent discussions in the AI community—most notably by Geoffrey Hinton—have highlighted a theoretical link between biological memory mechanisms (&amp;ldquo;Fast Weights&amp;rdquo;) and efficient engineering solutions like Linear Transformers and State Space Models (SSMs).&lt;/p&gt;
&lt;p&gt;My work focuses on &lt;strong&gt;Infrastructure Performance and Customer Engineering&lt;/strong&gt;, specifically for &lt;strong&gt;GPUs and TPUs&lt;/strong&gt;. I lead teams that bridge the gap between cutting-edge AI hardware and the latest ML models (like Gemini), ensuring optimal performance and reliability at Google Cloud scale. I thrive in the ambiguous space where hardware constraints meet software ambition—whether it&amp;rsquo;s debugging race conditions across thousands of chips or designing API surfaces for next-gen models.&lt;/p&gt;</description></item><item><title>The Convergence of Fast Weights, Linear Attention, and State Space Models</title><link>https://ericxliu.me/posts/the-convergence-of-fast-weights-linear-attention-and-state-space-models/</link><pubDate>Fri, 19 Dec 2025 00:00:00 +0000</pubDate><guid>https://ericxliu.me/posts/the-convergence-of-fast-weights-linear-attention-and-state-space-models/</guid><description>&lt;p&gt;Modern Large Language Models (LLMs) are dominated by the Transformer architecture. However, as context windows grow, the computational cost of the Transformer’s attention mechanism has become a primary bottleneck. Recent discussions in the AI community—most notably by Geoffrey Hinton—have highlighted a theoretical link between biological memory mechanisms (&amp;ldquo;Fast Weights&amp;rdquo;) and efficient engineering solutions like Linear Transformers and State Space Models (SSMs).&lt;/p&gt;
&lt;p&gt;This article explores the mathematical equivalence between Hinton’s concept of Fast Weights as Associative Memory and the recurrence mechanisms found in models such as Mamba and RWKV.&lt;/p&gt;</description></item><item><title>vAttention</title><link>https://ericxliu.me/posts/vattention/</link><pubDate>Mon, 08 Dec 2025 00:00:00 +0000</pubDate><guid>https://ericxliu.me/posts/vattention/</guid><description>&lt;p&gt;Large Language Model (LLM) inference is memory-bound, primarily due to the Key-Value (KV) cache—a store of intermediate state that grows linearly with sequence length. Efficient management of this memory is critical for throughput. While &lt;strong&gt;PagedAttention&lt;/strong&gt; (popularized by vLLM) became the industry standard by solving memory fragmentation via software, recent research suggests that leveraging the GPU’s native hardware Memory Management Unit (MMU) offers a more performant and portable solution.&lt;/p&gt;
&lt;h4 id="the-status-quo-pagedattention-and-software-tables"&gt;
The Status Quo: PagedAttention and Software Tables