<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Tinyml on TiGrIS</title><link>https://tigris-ml.dev/tags/tinyml/</link><description>Recent content in Tinyml on TiGrIS</description><generator>Hugo -- gohugo.io</generator><language>en</language><copyright>Copyright (c) 2025-2026 raws labs</copyright><lastBuildDate>Thu, 11 Jun 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://tigris-ml.dev/tags/tinyml/index.xml" rel="self" type="application/rss+xml"/><item><title>Static Memory Planning for Embedded Neural Network Inference</title><link>https://tigris-ml.dev/blog/static-memory-planning/</link><pubDate>Thu, 11 Jun 2026 00:00:00 +0000</pubDate><guid>https://tigris-ml.dev/blog/static-memory-planning/</guid><description>Peak working memory, not model size, decides whether inference fits in RAM. It is a property of the schedule and the allocation rather than the model alone, which makes it a compile-time planning problem.</description></item></channel></rss>