<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Split Computing | Jared Macshane</title><link>https://jared-mac.github.io/tags/split-computing/</link><atom:link href="https://jared-mac.github.io/tags/split-computing/index.xml" rel="self" type="application/rss+xml"/><description>Split Computing</description><generator>Hugo Blox Builder (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Thu, 01 Jan 2026 00:00:00 +0000</lastBuildDate><image><url>https://jared-mac.github.io/media/icon_hu_645fa481986063ef.png</url><title>Split Computing</title><link>https://jared-mac.github.io/tags/split-computing/</link></image><item><title>MANTIS: Modulated Adaptive Neural Compression for Task-Informed Split Computing</title><link>https://jared-mac.github.io/publication/preprint/mantis/</link><pubDate>Thu, 01 Jan 2026 00:00:00 +0000</pubDate><guid>https://jared-mac.github.io/publication/preprint/mantis/</guid><description>&lt;p>MANTIS evaluates task-conditioned split compression on UAVid semantic segmentation, WAID wildlife detection, and Boreal Fire smoke detection. The system improves the low- and mid-rate task-utility frontier, reducing bitrate by up to 62.2% at matched downstream accuracy and improving average normalized task accuracy by up to 9.3% at matched bitrate relative to the best non-conditioned or static baseline at the matched comparison point.&lt;/p></description></item><item><title>MANTIS Neural Compression</title><link>https://jared-mac.github.io/project/mantis/</link><pubDate>Wed, 01 Jan 2025 00:00:00 +0000</pubDate><guid>https://jared-mac.github.io/project/mantis/</guid><description>&lt;p>MANTIS is a task-informed split-computing system for UAV perception. It compresses visual evidence before transmission by estimating task relevance on the client, modulating the neural analysis transform, entropy-coding the resulting latent, and routing the compact representation to task-specific edge heads.&lt;/p>
&lt;p>The system is evaluated across UAV-relevant workloads:&lt;/p>
&lt;ul>
&lt;li>UAVid semantic segmentation for urban scene parsing&lt;/li>
&lt;li>WAID wildlife detection for aerial environmental monitoring&lt;/li>
&lt;li>Boreal Fire smoke detection for wildfire intelligence&lt;/li>
&lt;/ul>
&lt;p>Compared with JPEG, WebP, LADON, learned image codecs, and non-conditioned ablations, MANTIS improves the low- and mid-rate task-utility frontier. The paper reports up to 62.2% bitrate reduction at matched downstream accuracy and up to 9.3% average normalized task-accuracy improvement at matched bitrate.&lt;/p>
&lt;p>
&lt;figure >
&lt;div class="flex justify-center ">
&lt;div class="w-100" >&lt;img src="https://jared-mac.github.io/uploads/mantis/architecture.png" alt="MANTIS architecture" loading="lazy" data-zoomable />&lt;/div>
&lt;/div>&lt;/figure>
&lt;/p>
&lt;p>
&lt;figure >
&lt;div class="flex justify-center ">
&lt;div class="w-100" >&lt;img src="https://jared-mac.github.io/uploads/mantis/e2e-latency.png" alt="End-to-end latency across task workloads" loading="lazy" data-zoomable />&lt;/div>
&lt;/div>&lt;/figure>
&lt;/p></description></item></channel></rss>