add pearson and huang HPEC 2019 papers

content/publication/20190926_almasri_hpec/index.md

@@ -1,7 +1,7 @@
 +++
 title = "Upate on k-truss Dcomposition on GPU"
 date = 2019-08-22T00:00:00  # Schedule page publish date.
-draft = true
+draft = false
 
 # Authors. Comma separated list, e.g. `["Bob Smith", "David Jones"]`.
 authors = ["Mohammad Almasri", "Omer Anjum", "Carl Pearson", "Vikram S. Mailthody", "Zaid Qureshi", "Rakesh Nagi", "Jinjun Xiong", "Wen-Mei Hwu"]
@@ -45,7 +45,7 @@ selected = false
 #   E.g. `projects = ["deep-learning"]` references 
 #   `content/project/deep-learning/index.md`.
 #   Otherwise, set `projects = []`.
-projects = ["scope"]
+projects = []
 
 # Links (optional)
 url_pdf = ""

content/publication/20190926_huang_hpec/index.md

@@ -0,0 +1,71 @@
++++
+title = "Accelerating Sparse Deep Neural Networks on FPGAs"
+date = 2019-09-26T00:00:00  # Schedule page publish date.
+draft = false
+
+# Authors. Comma separated list, e.g. `["Bob Smith", "David Jones"]`.
+authors = ["Sitao Huang", "Carl Pearson", "Rakesh Nagi", "Jinjun Xiong", "Deming Chen", "Wen-Mei Hwu"]
+
+# Publication type.
+# Legend:
+# 0 = Uncategorized
+# 1 = Conference paper
+# 2 = Journal article
+# 3 = Manuscript
+# 4 = Report
+# 5 = Book
+# 6 = Book section
+publication_types = ["1"]
+
+# Publication name and optional abbreviated version.
+publication = ""
+publication_short = "In *HPEC'19*"
+
+# Abstract and optional shortened version.
+abstract = """
+Deep neural networks (DNNs) have been widely adopted in many domains, including computer vision, natural language processing, and medical care. Recent research revealsthat sparsity in DNN parameters can be exploited to reduce inference computational complexity and improve network quality. However, sparsity also introduces irregularity and extra complexity in data processing, which make the accelerator design challenging. This work presents the design and implementation of a highly flexible sparse DNN inference accelerator on FPGA.Our proposed inference engine can be easily configured to beused in both mobile computing and high-performance computing scenarios. Evaluation shows our proposed inference engine effectively accelerates sparse DNNs and outperforms CPU solution by up to 4.7x in terms of energy efficiency.
+"""
+abstract_short = ""
+
+
+# Does this page contain LaTeX math? (true/false)
+math = false
+
+# Does this page require source code highlighting? (true/false)
+highlight = false
+
+# Featured image thumbnail (optional)
+image_preview = ""
+
+# Is this a selected publication? (true/false)
+selected = true
+
+# Projects (optional).
+#   Associate this publication with one or more of your projects.
+#   Simply enter your project's folder or file name without extension.
+#   E.g. `projects = ["deep-learning"]` references 
+#   `content/project/deep-learning/index.md`.
+#   Otherwise, set `projects = []`.
+projects = []
+
+# Links (optional)
+url_pdf = "pdf/2019_huang_hpec.pdf"
+url_preprint = ""
+url_code = ""
+url_dataset = ""
+url_project = ""
+url_slides = ""
+url_video = ""
+url_poster = ""
+url_source = ""
+
+# Featured image
+# To use, add an image named `featured.jpg/png` to your page's folder. 
+[image]
+  # Caption (optional)
+  caption = ""
+
+  # Focal point (optional)
+  # Options: Smart, Center, TopLeft, Top, TopRight, Left, Right, BottomLeft, Bottom, BottomRight
+  focal_point = ""
++++

content/publication/20190926_pearson_hpec/index.md

@@ -1,7 +1,7 @@
 +++
 title = "Upate on Triangle Counting on GPU"
 date = 2019-08-22T00:00:00  # Schedule page publish date.
-draft = true
+draft = false
 
 # Authors. Comma separated list, e.g. `["Bob Smith", "David Jones"]`.
 authors = ["Carl Pearson", "Mohammad Almasri", "Omer Anjum", "Vikram S. Mailthody", "Zaid Qureshi", "Rakesh Nagi", "Jinjun Xiong", "Wen-Mei Hwu"]
@@ -23,6 +23,7 @@ publication_short = "In *HPEC'19*"
 
 # Abstract and optional shortened version.
 abstract = """
+This work presents an update to the triangle-counting portion of the subgraph isomorphism static graph challenge.  This  work  is  motivated  by  a  desire  to  understand the  impact  of  CUDA  unified  memory on the triangle-counting problem. First, CUDA unified memory is used to overlap reading large graph data from disk with graph data structures in GPU memory. Second, we use CUDA unified memory hintsto solve multi-GPU performance scaling challenges present in our last submission. Finally, we improve the single-GPU kernel performance from our past submission by introducing a work-stealing dynamic algorithm GPU kernel with persistent threads, which makes performance adaptive for large graphs withoutrequiring a graph analysis phase.
 """
 abstract_short = ""
 
@@ -45,10 +46,10 @@ selected = true
 #   E.g. `projects = ["deep-learning"]` references 
 #   `content/project/deep-learning/index.md`.
 #   Otherwise, set `projects = []`.
-projects = ["scope"]
+projects = []
 
 # Links (optional)
-url_pdf = ""
+url_pdf = "pdf/2019_pearson_hpec.pdf"
 url_preprint = ""
 url_code = ""
 url_dataset = ""