<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>DSPACE on SoloSoft</title><link>https://www.solosoft.dev/tags/dspace/</link><description>Recent content in DSPACE on SoloSoft</description><generator>Hugo</generator><language>en-us</language><atom:link href="https://www.solosoft.dev/tags/dspace/index.xml" rel="self" type="application/rss+xml"/><item><title>Beamr and dSPACE Validate Machine Learning-Safe Compression Technology, Set to R</title><link>https://www.solosoft.dev/trends/2026-04-21-beamr-validates-ml-safe-compression-for-dspace-dat/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://www.solosoft.dev/trends/2026-04-21-beamr-validates-ml-safe-compression-for-dspace-dat/</guid><description>&lt;h2 id="why-is-compression-becoming-the-next-arms-race-in-the-autonomous-vehicle-competition"&gt;Why Is &amp;ldquo;Compression&amp;rdquo; Becoming the Next Arms Race in the Autonomous Vehicle Competition?&lt;/h2&gt;
&lt;p&gt;&lt;strong&gt;Simple answer: because data costs are stifling the pace of innovation.&lt;/strong&gt; When a single autonomous test vehicle generates several terabytes of data per day, and fleets often consist of hundreds of vehicles, companies face not just a technical challenge, but an economic one. The infrastructure costs for storing, transmitting, and processing this data grow exponentially, yet the speed of development iteration is bottlenecked by the throughput of the data pipeline. The maturation of ML-Safe compression technology means we can physically &amp;ldquo;shrink&amp;rdquo; the scale of the problem, freeing precious computational resources and engineering time from the drudgery of data management and refocusing them on algorithmic innovation.&lt;/p&gt;</description></item></channel></rss>