Merge pull request #15 from hls-fpga-machine-learning/nvt/mar12

add esp4ml, h2rc talk, fix links, add workshop notice

Author: nhanvtran

index.html

@@ -55,26 +55,26 @@ <h2>about the Fast ML Lab</h2>
 		<div class="major">
 			<h2>Learn more!</h2>
 			<span class="byline">
-				Read a short <a href="images/coproc_whitepaper_v0.pdf">white paper</a> about how accelerated ML can be applied across many fields of fundamental physics! 
+				Read a short <a href="images/coproc_whitepaper_v0.pdf">white paper</a> about how accelerated ML can be applied across many fields of fundamental physics! Our first <a href="https://indico.cern.ch/event/822126/"> international workshop </a> was hosted at Fermilab in September 2019.  Lookout for our 2020 workshop announcement! 
 			</span>
 		</div>
 		<div class="column1">
 			<a href="#" class="image image-full"><img src="images/hls4ml.png" width=282 alt="" /></a>
-			<div class="box" height=300>
+			<div class="box" height=375>
 				<p>hls4ml: an open-source code framework for translating machine learning algorithms directly into FPGA firmware</p>
 				<a href="https://fastmachinelearning.org/hls4ml/" class="button">hls4ml home</a>
 			</div>
 		</div>			
 		<div class="column2">
 			<a href="#" class="image image-full"><img src="images/xilinx.png" width=282 alt="" /></a>
-			<div class="box" height=300>
+			<div class="box" height=375>
 				<p>A story from Xilinx on how we use high level synthesis to find the best events at the Large Hadron Collider</p>
 				<a href="https://www.xilinx.com/publications/powered-by-xilinx/cerncasestudy-final.pdf" class="button">Visit Xilinx</a>
 			</div>
 		</div>			
 		<div class="column3">
 			<a href="#" class="image image-full"><img src="images/azure.png" width=282 alt="" /></a>
-			<div class="box" height=300>
+			<div class="box" height=375>
 				<p>Microsoft Azure collaboration deploying FPGAs to accelerate ML to prototype computing solutions for future big science experiments</p>
 				<a href="https://customers.microsoft.com/en-us/story/724137-fermilab-led-team-tests-azure-ai-for-particle-physics-data-challenge" class="button">Visit Microsoft</a>
 				<a href="https://news.fnal.gov/2019/08/a-glimpse-into-the-future-accelerated-computing-for-accelerated-particles/" class="button">Fermi News</a>
@@ -83,7 +83,7 @@ <h2>Learn more!</h2>
 		</div>			
 		<div class="column4">
 			<a href="#" class="image image-full"><img src="images/zenuity.png" width=282 alt="" /></a>
-			<div class="box" height=300>
+			<div class="box" height=375>
 				<p>Zenuity has become the first automotive to team up with CERN to develop ML for autonomous drive cars</p>
 				<a href="https://www.prnewswire.co.uk/news-releases/zenuity-and-cern-team-up-on-fast-machine-learning-for-autonomous-driving-855081160.html" class="button">See the press</a>
 			</div>
@@ -100,28 +100,28 @@ <h2>Explore</h2>
 		<div class="column1">
 			<span class="icon icon-git-fork"></span>
 			<div class="title">
-				<h2>Projects</h2>
+				<h2> <a href="projects.html"> Papers,Talks,Videos </a></h2>
 				<span class="byline"></span>
 			</div>
 		</div>
 		<div class="column2">
 			<span class="icon icon-user"></span>
 			<div class="title">
-				<h2>People</h2>
+				<h2> <a href="people.html"> People </a> </h2>
 				<span class="byline"></span>
 			</div>
 		</div>
 		<div class="column3">
 			<span class="icon icon-building"></span>
 			<div class="title">
-				<h2>Papers/Talks</h2>
+				<h2> <a href="collaboration.html"> Join </a></h2>
 				<span class="byline"></span>
 			</div>
 		</div>
 		<div class="column4">
 			<span class="icon icon-envelope"></span>
 			<div class="title">
-				<h2>Contact</h2>
+				<h2><a href="mailto:fml@fastmachinelearning.org"> Contact </a></h2>
 				<span class="byline"></span>
 			</div>
 		</div>

projects.html

@@ -49,16 +49,18 @@ <h3> Papers </h3>
 		<p style="line-height:1.3">
 			<ul>
 				<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>
 		</p>
-		<h3> Talks </h3>
+		<h3> Talks/Videos </h3>
 		<p style="line-height:1.3">
 		List of various presentations from the community<br>
 		<ul>
+			<li> P. Harris, ML Acceleration with Heterogeneous Computing for Big Data Physics Experiments, Heterogeneous High Performance Computing Workshop at Supercomputing 2019, <a href="https://h2rc.cse.sc.edu/slides/invited1_Harris.pdf">slides</a></li>
 			<li> K. Pedro, FPGA-accelerated machine learning inference as a service for particle physics computing, CHEP 2019, <a href="https://indico.cern.ch/event/773049/contributions/3474731/">slides</a></li>
 			<li> J. Duarte, Machine Learning on FPGAs for low latency and high throughput inference, eScience 2019, <a href="https://escience2019.sched.com/event/Uuiy/machine-learning-on-fpgas-for-low-latency-and-high-throughput-inference?iframe=yes&w=100%&sidebar=yes&bg=no#">slides</a></li>
 			<li> M. Liu, FPGA-accelerated machine learning inference as a solution for particle physics computing challenges, PASC 2019, <a href="https://pasc19.pasc-conference.org/program/schedule/presentation/?id=msa134&sess=sess164">slides</a></li>