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feed.xml

@@ -1,4 +1,4 @@
-<?xml version="1.0" encoding="utf-8"?><feed xmlns="http://www.w3.org/2005/Atom" xml:lang="en"><generator uri="https://jekyllrb.com/" version="4.3.2">Jekyll</generator><link href="https://shaoleiren.github.io/feed.xml" rel="self" type="application/atom+xml"/><link href="https://shaoleiren.github.io/" rel="alternate" type="text/html" hreflang="en"/><updated>2023-11-22T06:27:52+00:00</updated><id>https://shaoleiren.github.io/feed.xml</id><title type="html">blank</title><subtitle></subtitle><entry><title type="html">a post with bibliography</title><link href="https://shaoleiren.github.io/blog/2023/post-bibliography/" rel="alternate" type="text/html" title="a post with bibliography"/><published>2023-07-12T13:56:00+00:00</published><updated>2023-07-12T13:56:00+00:00</updated><id>https://shaoleiren.github.io/blog/2023/post-bibliography</id><content type="html" xml:base="https://shaoleiren.github.io/blog/2023/post-bibliography/"><![CDATA[<p>This post shows how to add bibliography to simple blog posts. If you would like something more academic, check the <a href="/blog/2021/distill/">distill style post</a>.</p>]]></content><author><name></name></author><category term="sample-posts"/><category term="formatting"/><category term="bib"/><summary type="html"><![CDATA[an example of a blog post with bibliography]]></summary></entry><entry><title type="html">a post with jupyter notebook</title><link href="https://shaoleiren.github.io/blog/2023/jupyter-notebook/" rel="alternate" type="text/html" title="a post with jupyter notebook"/><published>2023-07-04T12:57:00+00:00</published><updated>2023-07-04T12:57:00+00:00</updated><id>https://shaoleiren.github.io/blog/2023/jupyter-notebook</id><content type="html" xml:base="https://shaoleiren.github.io/blog/2023/jupyter-notebook/"><![CDATA[<p>To include a jupyter notebook in a post, you can use the following code:</p> <div class="language-html highlighter-rouge"><div class="highlight"><pre class="highlight"><code>{::nomarkdown}
+<?xml version="1.0" encoding="utf-8"?><feed xmlns="http://www.w3.org/2005/Atom" xml:lang="en"><generator uri="https://jekyllrb.com/" version="4.3.2">Jekyll</generator><link href="https://shaoleiren.github.io/feed.xml" rel="self" type="application/atom+xml"/><link href="https://shaoleiren.github.io/" rel="alternate" type="text/html" hreflang="en"/><updated>2023-11-22T06:31:04+00:00</updated><id>https://shaoleiren.github.io/feed.xml</id><title type="html">blank</title><subtitle></subtitle><entry><title type="html">a post with bibliography</title><link href="https://shaoleiren.github.io/blog/2023/post-bibliography/" rel="alternate" type="text/html" title="a post with bibliography"/><published>2023-07-12T13:56:00+00:00</published><updated>2023-07-12T13:56:00+00:00</updated><id>https://shaoleiren.github.io/blog/2023/post-bibliography</id><content type="html" xml:base="https://shaoleiren.github.io/blog/2023/post-bibliography/"><![CDATA[<p>This post shows how to add bibliography to simple blog posts. If you would like something more academic, check the <a href="/blog/2021/distill/">distill style post</a>.</p>]]></content><author><name></name></author><category term="sample-posts"/><category term="formatting"/><category term="bib"/><summary type="html"><![CDATA[an example of a blog post with bibliography]]></summary></entry><entry><title type="html">a post with jupyter notebook</title><link href="https://shaoleiren.github.io/blog/2023/jupyter-notebook/" rel="alternate" type="text/html" title="a post with jupyter notebook"/><published>2023-07-04T12:57:00+00:00</published><updated>2023-07-04T12:57:00+00:00</updated><id>https://shaoleiren.github.io/blog/2023/jupyter-notebook</id><content type="html" xml:base="https://shaoleiren.github.io/blog/2023/jupyter-notebook/"><![CDATA[<p>To include a jupyter notebook in a post, you can use the following code:</p> <div class="language-html highlighter-rouge"><div class="highlight"><pre class="highlight"><code>{::nomarkdown}
 {% assign jupyter_path = "assets/jupyter/blog.ipynb" | relative_url %}
 {% capture notebook_exists %}{% file_exists assets/jupyter/blog.ipynb %}{% endcapture %}
 {% if notebook_exists == "true" %}

index.html

@@ -33,6 +33,13 @@
   <span class="na">month</span> <span class="p">=</span> <span class="s">{}</span><span class="p">,</span>
   <span class="na">year</span> <span class="p">=</span> <span class="s">{2023}</span><span class="p">,</span>
   <span class="na">url</span> <span class="p">=</span> <span class="s">{https://arxiv.org/abs/2307.10524}</span><span class="p">,</span>
+<span class="p">}</span></code></pre></figure> </div> </div> </div> </li> <li> <div class="row"> <div class="col-sm-2 abbr"><abbr class="badge">ASPLOS</abbr></div> <div id="MicroVSA_ASPLOS_2024" class="col-sm-10"> <div class="title"><a href="">MicroVSA: An Ultra-Lightweight Vector Symbolic Architecture-based Classifier Library for Tiny Microcontrollers</a></div> <div class="author"> Nuntipat Narkthong, Shijin Duan, <em>Shaolei Ren</em>, and Xiaolin Xu</div> <div class="periodical"> <em>ASPLOS</em>, 2024 </div> <div class="periodical"> </div> <div class="links"> <a class="abstract btn btn-sm z-depth-0" role="button">Abstract</a> <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a> <a href="" class="btn btn-sm z-depth-0" role="button">HTML</a> </div> <div class="badges"> </div> <div class="abstract hidden"> <p>Artificial intelligence has become feasible on tiny edge devices, thanks to the availability of high-performance microcontroller units (MCUs) and the development of highly-efficient machine learning (ML) models. Although successful, the current design practice implementing high-performance ML models on high-end MCU devices, still presents barriers that prohibit the more widespread adoption of ML on real-world applications built on low-end devices, where power and available hardware resources have been critical factors for including ML functionality in the final product. On the one hand, these low-end MCU devices have very limited computational resources in terms of hardware resources and power budget, which makes ML inference less accessible. On the other hand, these low-end and low-cost MCUs present the leading physical carrier of real-world smart applications, making small and fast-responsive (i.e., always-on) ML models an urgent need. This paper presents MicroVSA, a library of optimized implementations of a low-dimensional computing (LDC) classifier, a recently proposed variant of vector symbolic architecture (VSA), for 8-bit, 16-bit, and 32-bit MCUs. MicroVSA achieves 1.33–10.27x speedup from the vanilla LDC, and our sample model achieves 85–93% accuracy on three most common classification tasks for always-on inference on the MCUs — myocardial infarction detection, human activity recognition, and hot word detection — while requiring only a few tens of bytes of RAM and fitting in the vast majority of tiny 8-bit MCUs. For instance, our model for detecting the phrase "Hey Snapdragon" only needs 6.75KB of flash and 0.02KB of RAM and can complete one inference in 10.6 ms on a low-end 8-bit AVR MCU or 0.68 ms on an ARM Cortex-M4 MCU, outperforming the best neural network-based model by a factor of 7.2x-113x, making it the first to run on an 8-bit MCUs and the most efficient hot word detection model. Our study suggests that ubiquitous ML deployment on extremely low-cost MCUs is possible and that more study on VSA model training, model compression, and implementation techniques is needed to further explore the possibility of lowering the cost and power of ML on edge devices.</p> </div> <div class="bibtex hidden"> <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@article</span><span class="p">{</span><span class="nl">MicroVSA_ASPLOS_2024</span><span class="p">,</span>
+  <span class="na">title</span> <span class="p">=</span> <span class="s">{MicroVSA: An Ultra-Lightweight Vector Symbolic Architecture-based Classifier Library for Tiny Microcontrollers}</span><span class="p">,</span>
+  <span class="na">author</span> <span class="p">=</span> <span class="s">{Narkthong, Nuntipat and Duan, Shijin and Ren, Shaolei and Xu, Xiaolin}</span><span class="p">,</span>
+  <span class="na">journal</span> <span class="p">=</span> <span class="s">{ASPLOS}</span><span class="p">,</span>
+  <span class="na">month</span> <span class="p">=</span> <span class="s">{}</span><span class="p">,</span>
+  <span class="na">year</span> <span class="p">=</span> <span class="s">{2024}</span><span class="p">,</span>
+  <span class="na">url</span> <span class="p">=</span> <span class="s">{}</span><span class="p">,</span>
 <span class="p">}</span></code></pre></figure> </div> </div> </div> </li> <li> <div class="row"> <div class="col-sm-2 abbr"><abbr class="badge">ICML</abbr></div> <div id="Learning_OBM_ICML_2023" class="col-sm-10"> <div class="title"><a href="https://arxiv.org/abs/2306.00172" rel="external nofollow noopener" target="_blank">Learning for Edge-Weighted Online Bipartite Matching with Robustness Guarantees</a></div> <div class="author"> Pengfei Li, Jianyi Yang, and <em>Shaolei Ren</em> </div> <div class="periodical"> <em>ICML</em>, 2023 </div> <div class="periodical"> </div> <div class="links"> <a class="abstract btn btn-sm z-depth-0" role="button">Abstract</a> <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a> <a href="https://icml.cc/virtual/2023/poster/24251" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">HTML</a> </div> <div class="badges"> </div> <div class="abstract hidden"> <p>Many problems, such as online ad display, can be formulated as online bipartite matching. The crucial challenge lies in the nature of sequentially-revealed online item information, based on which we make irreversible matching decisions at each step. While numerous expert online algorithms have been proposed with bounded worst-case competitive ratios, they may not offer satisfactory performance in average cases. On the other hand, reinforcement learning (RL) has been applied to improve the average performance, but it lacks robustness and can perform arbitrarily poorly. In this paper, we propose a novel RL-based approach to edge-weighted online bipartite matching with robustness guarantees (LOMAR), achieving both good average-case and worst-case performance. The key novelty of LOMAR is a new online switching operation which, based on a judicious condition to hedge against future uncertainties, decides whether to follow the expert’s decision or the RL decision for each online item. We prove that for any ρ∈[0,1], LOMAR is ρ-competitive against any given expert online algorithm. To improve the average performance, we train the RL policy by explicitly considering the online switching operation. Finally, we run empirical experiments to demonstrate the advantages of LOMAR compared to existing baselines.</p> </div> <div class="bibtex hidden"> <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@article</span><span class="p">{</span><span class="nl">Learning_OBM_ICML_2023</span><span class="p">,</span>
   <span class="na">title</span> <span class="p">=</span> <span class="s">{Learning for Edge-Weighted Online Bipartite Matching with Robustness Guarantees}</span><span class="p">,</span>
   <span class="na">author</span> <span class="p">=</span> <span class="s">{Li, Pengfei and Yang, Jianyi and Ren, Shaolei}</span><span class="p">,</span>