Merge pull request #17 from hls-fpga-machine-learning/projects_update

update projects

Author: jmduarte

projects.html

@@ -48,14 +48,17 @@ <h2>Projects</h2>
 		<h3> Papers </h3>
 		<p style="line-height:1.3">
 		  <ul>
-		                <li> Compressing deep neural networks on FPGAs to binary and ternary precision with hls4ml, <a href="https://arxiv.org/abs/2003.06308">arXiv:2003.06308 [cs.LG]</a>, March 2020. </li>
-				<li> Fast inference of Boosted Decision Trees in FPGAs for particle physics, <a href="https://arxiv.org/abs/2002.02534">arXiv:2002.02534 [physics.comp-ph]</a>, February 2020. </li>
-				<li> ESP4ML: Platform-Based Design of Systems-on-Chip for Embedded Machine Learning, <a href="https://sld.cs.columbia.edu/pubs/giri_date20.pdf"> DATE Conference 2020 </a>.
-				<li> Accelerated Machine Learning as a Service for Particle Physics Computing, <a href="https://ml4physicalsciences.github.io/files/NeurIPS_ML4PS_2019_64.pdf">NeurIPS ML4PS Workshop, 2019</a>. </li>				
-				<li> Low-latency machine learning inference on FPGAs, <a href="https://ml4physicalsciences.github.io/files/NeurIPS_ML4PS_2019_74.pdf">NeurIPS ML4PS Workshop, 2019</a>. </li>				
-				<li> Fast inference of deep neural networks in FPGAs for particle physics, <a href="https://doi.org/10.1088/1748-0221/13/07/P07027">JINST 13, P07027 (2018)</a> </li>
-				<li> FPGA-accelerated machine learning inference as a service for particle physics computing, <a href="https://doi.org/10.1007/s41781-019-0027-2">Comput Softw Big Sci (2019) 3: 13</a> </li>
-			</ul>
+		    <li> Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physics, <a href="https://arxiv.org/abs/2008.03601">arXiv:2008.03601 [hep-ex]</a>. </li>
+		    <li> GPU coprocessors as a service for deep learning inference in high energy physics, <a href="https://arxiv.org/abs/2007.10359">arXiv:2007.10539 [physics.comp-ph]</a>. </li>
+		    <li> Ultra Low-latency, Low-area Inference Accelerators using Heterogeneous Deep Quantization with QKeras and hls4ml, <a href="https://arxiv.org/abs/2006.10159">arXiv:2006.10159 [physics.ins-det]</a>. </li>
+		    <li> Compressing deep neural networks on FPGAs to binary and ternary precision with hls4ml, <a href="https://doi.org/10.1088/2632-2153/aba042">MLST (2020)</a>. </li>
+		    <li> Fast inference of Boosted Decision Trees in FPGAs for particle physics, <a href="https://doi.org/10.1088/1748-0221/15/05/p05026">JINST 15, P05026 (2020)</a>. </li>
+		    <li> ESP4ML: Platform-Based Design of Systems-on-Chip for Embedded Machine Learning, <a href="https://sld.cs.columbia.edu/pubs/giri_date20.pdf"> DATE Conference 2020 </a>.
+		    <li> Accelerated Machine Learning as a Service for Particle Physics Computing, <a href="https://ml4physicalsciences.github.io/files/NeurIPS_ML4PS_2019_64.pdf">NeurIPS ML4PS Workshop 2019</a>. </li>
+		    <li> Low-latency machine learning inference on FPGAs, <a href="https://ml4physicalsciences.github.io/files/NeurIPS_ML4PS_2019_74.pdf">NeurIPS ML4PS Workshop 2019</a>. </li>				
+		    <li> Fast inference of deep neural networks in FPGAs for particle physics, <a href="https://doi.org/10.1088/1748-0221/13/07/P07027">JINST 13, P07027 (2018)</a>. </li>
+		    <li> FPGA-accelerated machine learning inference as a service for particle physics computing, <a href="https://doi.org/10.1007/s41781-019-0027-2">Comput. Softw. Big Sci. 3, 13 (2019)</a> </li>
+		    </ul>
 		</p>
 		<h3> Talks/Videos </h3>
 		<p style="line-height:1.3">