add uniql

Author: hychiang-git

_bibliography/papers.bib

@@ -1,6 +1,19 @@
 ---
 ---
-  
+
+@article{chiang2025uniql,
+  title={UniQL: Unified Quantization and Low-rank Compression for Adaptive Edge LLMs},
+  author={Chiang, Hung-Yueh and Chang, Chi-Chih and Lu, Yu-Chen and Lin, Chien-Yu and Wu, Kai-Chiang and Abdelfattah, Mohamed S. and Marculescu, Diana},
+  journal={arXiv preprint arXiv:2512.03383},
+  venue_type={arXiv},
+  venue_url={https://arxiv.org/abs/2512.03383},
+  year={2025},
+  pdf={https://arxiv.org/pdf/2512.03383.pdf},
+  preview={uniql_blog.jpg},
+  bibtex_show={true},
+  selected={true},
+}
+
 @inproceedings{chiang2025quamba2,
   title={Quamba2: A Robust and Scalable Post-training Quantization Framework for Selective State Space Models},
   author={Chiang, Hung-Yueh and Chang, Chi-Chih and Frumkin, Natalia and Wu, Kai-Chiang and Abdelfattah, Mohamed S. and Marculescu, Diana},

_projects/quamba2.md

@@ -5,7 +5,7 @@ full_title: "Quamba2: A Robust and Scalable Post-training Quantization Framework
 authors: Hung-Yueh Chiang, Chi-Chih Chang, Natalia Frumkin, Kai-Chiang Wu, Mohamed S. Abdelfattah, Diana Marculescu
 description: "A Quantization Framework for Selective State Space Models"
 img: assets/img/publication_preview/quamba2_blog.jpg
-importance: 1
+importance: 7
 category: research
 ---
 

_projects/uniql.md

@@ -0,0 +1,123 @@
+---
+layout: page
+title: UniQL
+full_title: "UniQL: Unified Quantization and Low-rank Compression for Adaptive Edge LLMs"
+authors: Hung-Yueh Chiang, Chi-Chih Chang, Yu-Chen Lu, Chien-Yu Lin, Kai-Chiang Wu, Mohamed S. Abdelfattah, Diana Marculescu
+description: "Quantization and Low-rank Compression for Edge LLMs"
+img: assets/img/publication_preview/uniql_blog.jpg
+importance: 1
+category: research
+---
+
+<style>  
+li {  
+    font-size: 1.1rem; /* Adjust as needed */  
+}  
+</style>  
+
+<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/4.7.0/css/font-awesome.min.css">
+
+<div style="text-align: center; padding-bottom: 1rem;">
+<!-- <abbr class="badge" style="background-color:#00369f; margin-left:0.1rem; margin-right:0.1rem; font-size:1.1rem;">Arxiv</abbr> -->
+<abbr class="badge" style="background-color:#BF5700; margin-left:0.1rem; margin-right:0.1rem; font-size:1.1rem; width:80px; display:inline-block; text-align:center;">Arxiv</abbr>
+</div>
+
+<div class="authors"> 
+    <a href="https://hychiang.info">Hung-Yueh Chiang</a><sup>1</sup>,
+    <a href="https://ccchang.info/">Chi-Chih Chang</a><sup>2</sup>, 
+    <a href="https://scholar.google.com/citations?user=iTlHWjwAAAAJ&hl=zh-TW">Yu-Chen Lu</a><sup>3</sup>,
+    <a href="https://cylinbao.github.io/">Chien-Yu Lin</a><sup>4</sup>,
+    <br>
+    <a href="https://people.cs.nycu.edu.tw/~kcw/">Kai-Chiang Wu</a><sup>3</sup>,
+    <a href="https://www.mohsaied.com/">Mohamed S. Abdelfattah</a><sup>2</sup>,
+    <a href="https://users.ece.utexas.edu/~dianam/">Diana Marculescu</a><sup>1</sup>
+</div>
+<div class="authors">
+    <sup>1</sup>The University of Texas at Austin,
+    <sup>2</sup>Cornell University, <br>
+    <sup>3</sup>National Yang Ming Chiao Tung University
+    <sup>4</sup>University of Washington
+</div>
+<div style="text-align: center; margin-top:12px;">
+    <a href="https://arxiv.org/abs/2512.03383"><i class="fa fa-file-pdf-o" style="font-size:24px;color"></i><b> Paper </b></a> &nbsp;
+    <a href="https://github.com/enyac-group/UniQL"><i class="fa fa-github" style="font-size:24px;color"></i><b> Code </b></a> &nbsp;
+    <a href="https://huggingface.co/ut-enyac"><span style="font-size: 22px;">&#129303;</span><b> Models </b></a>
+</div>
+
+
+<br>
+<div style="text-align: center;">
+<p style="font-family: Comic Neue; font-size: 1.4rem;">
+    📚 Unified support Transformers, SSMs, and hybrid models <br>
+    🔗 One-pass framework for quantization + structured low-rank pruning <br>
+    ⚡ <strong>2.7×–3.4×</strong> latency speedups, <strong>4×–5.7×</strong> memory reductions <br>
+</p>
+</div>
+<div class="row mt-3">
+    <div class="col-sm-12 mt-3 mt-md-0 offset-0">
+        {% include figure.html path="assets/img/projects/uniql/uniql.png" title="example image" class="img-fluid rounded z-depth-1" %}
+    </div>
+</div>
+<br>
+
+# Supporting for Transformer and Mamba blocks
+- Joint weight decomposition. (The group of weights is shown in the same background color)
+<div class="row">
+    <div class="col-sm mt-3 mt-md-0">
+        {% include gif.html path="assets/img/projects/uniql/modular.png" title="example image" class="img-fluid rounded z-depth-1" %}
+    </div>
+</div>
+<br>
+
+# Joint design quantization and structured pruning
+- Fused RoPE to support and accelerate pruned Q and K
+- Quantization-aware SVD decomposition to reduce the quantization errors
+<div class="row mt-3">
+    <div class="col-sm-10 mt-3 mt-md-0 offset-1">
+        {% include figure.html path="assets/img/projects/uniql/kernel_svd.png" title="example image" class="img-fluid rounded z-depth-1" %}
+    </div>
+</div>
+<br>
+
+# One-pass framework supporting all pruning rates
+- (a) <b>Pseudo-inverse-free</b>, <b>quantization-aware</b>, and <b>state-aware</b> matrix decomposition methods for the grouped weights to obtain sorted weights
+- (b) During fine-tuning, we sample global pruning rates, and masked out the weight channels 
+- (c) The refined patches are fused into the weights, followed by model quantization
+for deployment
+- (d) Based on the system utilization, we perform <b>on-device adaptive pruning</b> of the quantized model.
+<div class="row mt-4">
+    <div class="col-sm mt-3 mt-md-0 offset-0">
+        {% include figure.html path="assets/img/projects/uniql/one-pass.png" title="example image" class="img-fluid rounded z-depth-1" %}
+    </div>
+</div>
+<br>
+
+
+
+# Main results
+<div class="row mt-4">
+    <div class="col-sm-10 mt-4 mt-md-0 offset-1">
+        {% include figure.html path="assets/img/projects/uniql/main_results.png" title="example image" class="img-fluid rounded z-depth-1" %}
+    </div>
+</div>
+<br>
+
+
+
+
+# Citation
+{% raw %}
+```latex
+@article{chiang2025uniql,
+  title={UniQL: Unified Quantization and Low-rank Compression for Adaptive Edge LLMs},
+  author={Chiang, Hung-Yueh and Chang, Chi-Chih and Lu, Yu-Chen and Lin, Chien-Yu and Wu, Kai-Chiang and Abdelfattah, Mohamed S. and Marculescu, Diana},
+  journal={arXiv preprint arXiv:2512.03383},
+  year={2025},
+}
+
+```
+{% endraw %}
+
+<br>
+# Acknowledgements
+This work was supported in part by the ONR Minerva program, NSF CCF Grant No. 2107085, iMAGiNE - the Intelligent Machine Engineering Consortium at UT Austin, UT Cockrell School of Engineering Doctoral Fellowships, NSF CAREER Grant No. 2339084, Nvidia research gift, and Taiwan’s NSTC Grant No. 111-2221-E-A49-148-MY3.