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<article itemscope itemtype="http://schema.org/WebPage"><header style="display:none;"><h1>Home</h1></header><meta itemprop="headline" content="Home"><meta itemprop="author" content="Pengzhan Hao"/><div class="js-article-content"><div class="layout--articles"><div class="article-list items items--divided"><article class="item" itemscope itemtype="http://schema.org/BlogPosting"><div class="item__image" style="vertical-align: middle"><img class="image" src="/static/2021-10/edgelearn-1.png" /></div><div class="item__content">
<article itemscope itemtype="http://schema.org/WebPage"><header style="display:none;"><h1>Home</h1></header><meta itemprop="headline" content="Home"><meta itemprop="author" content="Pengzhan Hao"/><div class="js-article-content"><div class="layout--articles"><div class="article-list items items--divided"><article class="item" itemscope itemtype="http://schema.org/BlogPosting"><div class="item__image" style="vertical-align: middle"><img class="image" src="/static/2021-10/f.5_Impl_leader_worker.png" /></div><div class="item__content">
<header><a href="/posts/eddl-how-do-we-train-on-limited-edge-devices-part2"><h2 itemprop="headline" class="item__header">EDDL: How do we train neural networks on limited edge devices - PART 2</h2></a></header>
<div class="item__description"><div class="article__content" itemprop="description articleBody">In the last post, part1, our idea of distributed learning on edge environment was generally addressed.
I introduced the reason why edge distributed learning is needed and what improvements it can achieve.
In this post, I will talk about our motivation study and how our framework works.
How does data support us training on edge?
Before designin...</div><p><a href="/posts/eddl-how-do-we-train-on-limited-edge-devices-part2">Read more</a></p></div><div class="article__info clearfix"><ul class="left-col menu"><li>
<a class="button button--secondary button--pill button--sm"
href="/archive.html?tag=Research">Research</a>
</li></ul><ul class="right-col menu"><li><i class="fas fa-user"></i> <span>Pengzhan Hao</span></li><li><i class="far fa-calendar-alt"></i> <span>Oct 31, 2021</span>
</li></ul></div><meta itemprop="author" content="Pengzhan Hao"/><meta itemprop="datePublished" content="2021-10-31T13:01:14-04:00">
<meta itemprop="keywords" content="Research"></div>
</article><article class="item" itemscope itemtype="http://schema.org/BlogPosting"><div class="item__image" style="vertical-align: middle"><img class="image" src="/static/2021-10/edgelearn-1.png" /></div><div class="item__content">
<header><a href="/posts/eddl-how-do-we-train-on-limited-edge-devices"><h2 itemprop="headline" class="item__header">EDDL: How do we train neural networks on limited edge devices - PART 1</h2></a></header>
<div class="item__description"><div class="article__content" itemprop="description articleBody">This post introduces our previous milestone in project “Edge trainer”, as the paper “EDDL: A Distributed Deep Learning System for Resource-limited Edge Computing Environment.” was published.
As the first part of the introductions, I focus only on the motivation and summary of our works.
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</li></ul><ul class="right-col menu"><li><i class="fas fa-user"></i> <span>Pengzhan Hao</span></li><li><i class="far fa-calendar-alt"></i> <span>Sep 15, 2020</span>
</li></ul></div><meta itemprop="author" content="Pengzhan Hao"/><meta itemprop="datePublished" content="2020-09-15T22:00:14-04:00">
<meta itemprop="keywords" content="visualization"></div>
</article><article class="item" itemscope itemtype="http://schema.org/BlogPosting"><div class="item__content">
</article><article class="item" itemscope itemtype="http://schema.org/BlogPosting"><div class="item__image" style="vertical-align: middle"><img class="image" src="/static/2021-10/Xv6_LS_Command_Output.png" /></div><div class="item__content">
<header><a href="/posts/intro-xv6"><h2 itemprop="headline" class="item__header">Xv6 introduction</h2></a></header>
<div class="item__description"><div class="article__content" itemprop="description articleBody">In this post, you will learn a few basic concepts of xv6. Learning path will be closed coupled to first project assignment I gave when I assisted in teaching OS classes.
Understand system call and know how to implement a simple one will be coved as the first half.