<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>Ashiqur Rasul</title><link>https://rasulashiqur.github.io/</link><description>Recent content on Ashiqur Rasul</description><generator>Hugo -- gohugo.io</generator><language>en-us</language><atom:link href="https://rasulashiqur.github.io/index.xml" rel="self" type="application/rss+xml"/><item><title>ACE: Adaptive Compute-Efficient State Space Models</title><link>https://rasulashiqur.github.io/projects/ace/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://rasulashiqur.github.io/projects/ace/</guid><description>Dynamic token routing for Mamba models that adapts computation to token importance.</description></item><item><title>CASPER: Adaptive Retrieval with In-DRAM Processing</title><link>https://rasulashiqur.github.io/projects/casper/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://rasulashiqur.github.io/projects/casper/</guid><description>Confidence-guided dimension adaptivity and in-DRAM similarity search for efficient information retrieval.</description></item><item><title>Adaptive Testing of Compute-in-Memory GANs</title><link>https://rasulashiqur.github.io/projects/adaptive-cim-testing/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://rasulashiqur.github.io/projects/adaptive-cim-testing/</guid><description>Backpropagation-guided test compaction for faster testing of analog compute-in-memory systems.</description></item><item><title>Energy-Efficient Domain-Specific AI Models and Agents</title><link>https://rasulashiqur.github.io/projects/efficient-ai-agents/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://rasulashiqur.github.io/projects/efficient-ai-agents/</guid><description>A cross-layer perspective on domain-specific intelligence, brain-inspired computing, and efficient hardware.</description></item><item><title>Dynamic 3D CNN Pruning for Efficient Edge Inference</title><link>https://rasulashiqur.github.io/projects/dynamic-3d-cnn-pruning/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://rasulashiqur.github.io/projects/dynamic-3d-cnn-pruning/</guid><description>Input-dependent frame, channel, and feature adaptation to reduce the cost of video inference.</description></item></channel></rss>