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Posted to commits@tvm.apache.org by tq...@apache.org on 2020/03/20 03:36:29 UTC
[incubator-tvm-site] branch asf-site updated: Build at Thu Mar 19
20:36:18 PDT 2020
This is an automated email from the ASF dual-hosted git repository.
tqchen pushed a commit to branch asf-site
in repository https://gitbox.apache.org/repos/asf/incubator-tvm-site.git
The following commit(s) were added to refs/heads/asf-site by this push:
new 8dc48c6 Build at Thu Mar 19 20:36:18 PDT 2020
8dc48c6 is described below
commit 8dc48c632488e9cdda5ef118f4a4498b9d6af3f9
Author: tqchen <tq...@gmail.com>
AuthorDate: Thu Mar 19 20:36:18 2020 -0700
Build at Thu Mar 19 20:36:18 PDT 2020
---
2017/08/17/tvm-release-announcement.html | 6 ++--
...s-with-TVM-A-Depthwise-Convolution-Example.html | 6 ++--
2017/10/06/nnvm-compiler-announcement.html | 6 ++--
...s-to-TVM-Stack-and-NNVM-Compiler-with-ROCm.html | 6 ++--
2017/11/08/android-rpc-introduction.html | 6 ++--
2018/01/16/opt-mali-gpu.html | 6 ++--
2018/03/12/webgl.html | 6 ++--
2018/03/23/nmt-transformer-optimize.html | 6 ++--
2018/07/12/vta-release-announcement.html | 6 ++--
2018/08/10/DLPack-Bridge.html | 6 ++--
2018/10/03/auto-opt-all.html | 6 ++--
2018/10/09/ml-in-tees.html | 6 ++--
2018/12/18/lowprecision-conv.html | 6 ++--
2019/01/19/Golang.html | 6 ++--
2019/03/18/tvm-apache-announcement.html | 6 ++--
2019/04/29/opt-cuda-quantized.html | 6 ++--
2019/05/30/pytorch-frontend.html | 6 ++--
about.html | 4 +--
asf.html | 4 +--
atom.xml | 36 ++++++++++----------
blog.html | 4 +--
categories.html | 4 +--
community.html | 4 +--
index.html | 4 +--
rss.xml | 38 +++++++++++-----------
tags.html | 4 +--
vta.html | 4 +--
27 files changed, 104 insertions(+), 104 deletions(-)
diff --git a/2017/08/17/tvm-release-announcement.html b/2017/08/17/tvm-release-announcement.html
index 5ed205a..eb9e769 100644
--- a/2017/08/17/tvm-release-announcement.html
+++ b/2017/08/17/tvm-release-announcement.html
@@ -126,9 +126,9 @@
- <li> <a href="https://sampl.cs.washington.edu/tvmconf">TVM Conference</a></li>
+ <li> <a href="https://tvmconf.org">TVM Conference</a></li>
<li> <a href="https://docs.tvm.ai">Docs</a></li>
- <li> <a href="https://github.com/dmlc/tvm/">Github</a></li>
+ <li> <a href="https://github.com/apache/incubator-tvm/">Github</a></li>
<li> <a href="/asf">ASF</a></li>
</ul>
</div>
@@ -141,7 +141,7 @@
<div class="span14">
<h1>TVM: An End to End IR Stack for Deploying Deep Learning Workloads on Hardware Platforms </h1>
<p class="post-meta">
- <time datetime="2017-08-17T14:00:00-05:00" itemprop="datePublished">
+ <time datetime="2017-08-17T12:00:00-07:00" itemprop="datePublished">
Aug 17, 2017
</time>
diff --git a/2017/08/22/Optimize-Deep-Learning-GPU-Operators-with-TVM-A-Depthwise-Convolution-Example.html b/2017/08/22/Optimize-Deep-Learning-GPU-Operators-with-TVM-A-Depthwise-Convolution-Example.html
index 367287b..e59d6f1 100644
--- a/2017/08/22/Optimize-Deep-Learning-GPU-Operators-with-TVM-A-Depthwise-Convolution-Example.html
+++ b/2017/08/22/Optimize-Deep-Learning-GPU-Operators-with-TVM-A-Depthwise-Convolution-Example.html
@@ -126,9 +126,9 @@
- <li> <a href="https://sampl.cs.washington.edu/tvmconf">TVM Conference</a></li>
+ <li> <a href="https://tvmconf.org">TVM Conference</a></li>
<li> <a href="https://docs.tvm.ai">Docs</a></li>
- <li> <a href="https://github.com/dmlc/tvm/">Github</a></li>
+ <li> <a href="https://github.com/apache/incubator-tvm/">Github</a></li>
<li> <a href="/asf">ASF</a></li>
</ul>
</div>
@@ -141,7 +141,7 @@
<div class="span14">
<h1>Optimize Deep Learning GPU Operators with TVM: A Depthwise Convolution Example </h1>
<p class="post-meta">
- <time datetime="2017-08-22T00:00:00-05:00" itemprop="datePublished">
+ <time datetime="2017-08-22T00:00:00-07:00" itemprop="datePublished">
Aug 22, 2017
</time>
diff --git a/2017/10/06/nnvm-compiler-announcement.html b/2017/10/06/nnvm-compiler-announcement.html
index 93ee782..9d909b1 100644
--- a/2017/10/06/nnvm-compiler-announcement.html
+++ b/2017/10/06/nnvm-compiler-announcement.html
@@ -126,9 +126,9 @@
- <li> <a href="https://sampl.cs.washington.edu/tvmconf">TVM Conference</a></li>
+ <li> <a href="https://tvmconf.org">TVM Conference</a></li>
<li> <a href="https://docs.tvm.ai">Docs</a></li>
- <li> <a href="https://github.com/dmlc/tvm/">Github</a></li>
+ <li> <a href="https://github.com/apache/incubator-tvm/">Github</a></li>
<li> <a href="/asf">ASF</a></li>
</ul>
</div>
@@ -141,7 +141,7 @@
<div class="span14">
<h1>NNVM Compiler: Open Compiler for AI Frameworks </h1>
<p class="post-meta">
- <time datetime="2017-10-06T10:30:00-05:00" itemprop="datePublished">
+ <time datetime="2017-10-06T08:30:00-07:00" itemprop="datePublished">
Oct 6, 2017
</time>
diff --git a/2017/10/30/Bringing-AMDGPUs-to-TVM-Stack-and-NNVM-Compiler-with-ROCm.html b/2017/10/30/Bringing-AMDGPUs-to-TVM-Stack-and-NNVM-Compiler-with-ROCm.html
index b9edd7d..df54e7c 100644
--- a/2017/10/30/Bringing-AMDGPUs-to-TVM-Stack-and-NNVM-Compiler-with-ROCm.html
+++ b/2017/10/30/Bringing-AMDGPUs-to-TVM-Stack-and-NNVM-Compiler-with-ROCm.html
@@ -126,9 +126,9 @@
- <li> <a href="https://sampl.cs.washington.edu/tvmconf">TVM Conference</a></li>
+ <li> <a href="https://tvmconf.org">TVM Conference</a></li>
<li> <a href="https://docs.tvm.ai">Docs</a></li>
- <li> <a href="https://github.com/dmlc/tvm/">Github</a></li>
+ <li> <a href="https://github.com/apache/incubator-tvm/">Github</a></li>
<li> <a href="/asf">ASF</a></li>
</ul>
</div>
@@ -141,7 +141,7 @@
<div class="span14">
<h1>Bringing AMDGPUs to TVM Stack and NNVM Compiler with ROCm </h1>
<p class="post-meta">
- <time datetime="2017-10-30T00:00:00-05:00" itemprop="datePublished">
+ <time datetime="2017-10-30T00:00:00-07:00" itemprop="datePublished">
Oct 30, 2017
</time>
diff --git a/2017/11/08/android-rpc-introduction.html b/2017/11/08/android-rpc-introduction.html
index 7ffc0cf..9e7793a 100644
--- a/2017/11/08/android-rpc-introduction.html
+++ b/2017/11/08/android-rpc-introduction.html
@@ -126,9 +126,9 @@
- <li> <a href="https://sampl.cs.washington.edu/tvmconf">TVM Conference</a></li>
+ <li> <a href="https://tvmconf.org">TVM Conference</a></li>
<li> <a href="https://docs.tvm.ai">Docs</a></li>
- <li> <a href="https://github.com/dmlc/tvm/">Github</a></li>
+ <li> <a href="https://github.com/apache/incubator-tvm/">Github</a></li>
<li> <a href="/asf">ASF</a></li>
</ul>
</div>
@@ -141,7 +141,7 @@
<div class="span14">
<h1>Remote Profile and Test Deep Learning Cross Compilation on Mobile Phones with TVM RPC </h1>
<p class="post-meta">
- <time datetime="2017-11-08T00:00:00-06:00" itemprop="datePublished">
+ <time datetime="2017-11-08T00:00:00-08:00" itemprop="datePublished">
Nov 8, 2017
</time>
diff --git a/2018/01/16/opt-mali-gpu.html b/2018/01/16/opt-mali-gpu.html
index 44d0f57..ce5b303 100644
--- a/2018/01/16/opt-mali-gpu.html
+++ b/2018/01/16/opt-mali-gpu.html
@@ -126,9 +126,9 @@
- <li> <a href="https://sampl.cs.washington.edu/tvmconf">TVM Conference</a></li>
+ <li> <a href="https://tvmconf.org">TVM Conference</a></li>
<li> <a href="https://docs.tvm.ai">Docs</a></li>
- <li> <a href="https://github.com/dmlc/tvm/">Github</a></li>
+ <li> <a href="https://github.com/apache/incubator-tvm/">Github</a></li>
<li> <a href="/asf">ASF</a></li>
</ul>
</div>
@@ -141,7 +141,7 @@
<div class="span14">
<h1>Optimizing Mobile Deep Learning on ARM GPU with TVM </h1>
<p class="post-meta">
- <time datetime="2018-01-16T00:00:00-06:00" itemprop="datePublished">
+ <time datetime="2018-01-16T00:00:00-08:00" itemprop="datePublished">
Jan 16, 2018
</time>
diff --git a/2018/03/12/webgl.html b/2018/03/12/webgl.html
index cf3ac31..97d235f 100644
--- a/2018/03/12/webgl.html
+++ b/2018/03/12/webgl.html
@@ -126,9 +126,9 @@
- <li> <a href="https://sampl.cs.washington.edu/tvmconf">TVM Conference</a></li>
+ <li> <a href="https://tvmconf.org">TVM Conference</a></li>
<li> <a href="https://docs.tvm.ai">Docs</a></li>
- <li> <a href="https://github.com/dmlc/tvm/">Github</a></li>
+ <li> <a href="https://github.com/apache/incubator-tvm/">Github</a></li>
<li> <a href="/asf">ASF</a></li>
</ul>
</div>
@@ -141,7 +141,7 @@
<div class="span14">
<h1>Compiling Deep Learning Models to WebGL with TVM </h1>
<p class="post-meta">
- <time datetime="2018-03-12T00:00:00-05:00" itemprop="datePublished">
+ <time datetime="2018-03-12T00:00:00-07:00" itemprop="datePublished">
Mar 12, 2018
</time>
diff --git a/2018/03/23/nmt-transformer-optimize.html b/2018/03/23/nmt-transformer-optimize.html
index 12b9509..3a1ff9d 100644
--- a/2018/03/23/nmt-transformer-optimize.html
+++ b/2018/03/23/nmt-transformer-optimize.html
@@ -126,9 +126,9 @@
- <li> <a href="https://sampl.cs.washington.edu/tvmconf">TVM Conference</a></li>
+ <li> <a href="https://tvmconf.org">TVM Conference</a></li>
<li> <a href="https://docs.tvm.ai">Docs</a></li>
- <li> <a href="https://github.com/dmlc/tvm/">Github</a></li>
+ <li> <a href="https://github.com/apache/incubator-tvm/">Github</a></li>
<li> <a href="/asf">ASF</a></li>
</ul>
</div>
@@ -141,7 +141,7 @@
<div class="span14">
<h1>Bringing TVM into TensorFlow for Optimizing Neural Machine Translation on GPU </h1>
<p class="post-meta">
- <time datetime="2018-03-23T00:00:00-05:00" itemprop="datePublished">
+ <time datetime="2018-03-23T00:00:00-07:00" itemprop="datePublished">
Mar 23, 2018
</time>
diff --git a/2018/07/12/vta-release-announcement.html b/2018/07/12/vta-release-announcement.html
index 0d49756..4f79a5d 100644
--- a/2018/07/12/vta-release-announcement.html
+++ b/2018/07/12/vta-release-announcement.html
@@ -126,9 +126,9 @@
- <li> <a href="https://sampl.cs.washington.edu/tvmconf">TVM Conference</a></li>
+ <li> <a href="https://tvmconf.org">TVM Conference</a></li>
<li> <a href="https://docs.tvm.ai">Docs</a></li>
- <li> <a href="https://github.com/dmlc/tvm/">Github</a></li>
+ <li> <a href="https://github.com/apache/incubator-tvm/">Github</a></li>
<li> <a href="/asf">ASF</a></li>
</ul>
</div>
@@ -141,7 +141,7 @@
<div class="span14">
<h1>VTA: An Open, Customizable Deep Learning Acceleration Stack </h1>
<p class="post-meta">
- <time datetime="2018-07-12T00:00:00-05:00" itemprop="datePublished">
+ <time datetime="2018-07-12T00:00:00-07:00" itemprop="datePublished">
Jul 12, 2018
</time>
diff --git a/2018/08/10/DLPack-Bridge.html b/2018/08/10/DLPack-Bridge.html
index 0a7c9e5..c114d4d 100644
--- a/2018/08/10/DLPack-Bridge.html
+++ b/2018/08/10/DLPack-Bridge.html
@@ -126,9 +126,9 @@
- <li> <a href="https://sampl.cs.washington.edu/tvmconf">TVM Conference</a></li>
+ <li> <a href="https://tvmconf.org">TVM Conference</a></li>
<li> <a href="https://docs.tvm.ai">Docs</a></li>
- <li> <a href="https://github.com/dmlc/tvm/">Github</a></li>
+ <li> <a href="https://github.com/apache/incubator-tvm/">Github</a></li>
<li> <a href="/asf">ASF</a></li>
</ul>
</div>
@@ -141,7 +141,7 @@
<div class="span14">
<h1>Building a Cross-Framework Deep Learning Compiler via DLPack </h1>
<p class="post-meta">
- <time datetime="2018-08-10T00:00:00-05:00" itemprop="datePublished">
+ <time datetime="2018-08-10T00:00:00-07:00" itemprop="datePublished">
Aug 10, 2018
</time>
diff --git a/2018/10/03/auto-opt-all.html b/2018/10/03/auto-opt-all.html
index a1c5108..c311d3a 100644
--- a/2018/10/03/auto-opt-all.html
+++ b/2018/10/03/auto-opt-all.html
@@ -126,9 +126,9 @@
- <li> <a href="https://sampl.cs.washington.edu/tvmconf">TVM Conference</a></li>
+ <li> <a href="https://tvmconf.org">TVM Conference</a></li>
<li> <a href="https://docs.tvm.ai">Docs</a></li>
- <li> <a href="https://github.com/dmlc/tvm/">Github</a></li>
+ <li> <a href="https://github.com/apache/incubator-tvm/">Github</a></li>
<li> <a href="/asf">ASF</a></li>
</ul>
</div>
@@ -141,7 +141,7 @@
<div class="span14">
<h1>Automatic Kernel Optimization for Deep Learning on All Hardware Platforms </h1>
<p class="post-meta">
- <time datetime="2018-10-03T00:00:00-05:00" itemprop="datePublished">
+ <time datetime="2018-10-03T00:00:00-07:00" itemprop="datePublished">
Oct 3, 2018
</time>
diff --git a/2018/10/09/ml-in-tees.html b/2018/10/09/ml-in-tees.html
index 7b2d63d..06bba1e 100644
--- a/2018/10/09/ml-in-tees.html
+++ b/2018/10/09/ml-in-tees.html
@@ -126,9 +126,9 @@
- <li> <a href="https://sampl.cs.washington.edu/tvmconf">TVM Conference</a></li>
+ <li> <a href="https://tvmconf.org">TVM Conference</a></li>
<li> <a href="https://docs.tvm.ai">Docs</a></li>
- <li> <a href="https://github.com/dmlc/tvm/">Github</a></li>
+ <li> <a href="https://github.com/apache/incubator-tvm/">Github</a></li>
<li> <a href="/asf">ASF</a></li>
</ul>
</div>
@@ -141,7 +141,7 @@
<div class="span14">
<h1>Efficient Privacy-Preserving ML Using TVM </h1>
<p class="post-meta">
- <time datetime="2018-10-09T00:00:00-05:00" itemprop="datePublished">
+ <time datetime="2018-10-09T00:00:00-07:00" itemprop="datePublished">
Oct 9, 2018
</time>
diff --git a/2018/12/18/lowprecision-conv.html b/2018/12/18/lowprecision-conv.html
index e2f8300..1aecef5 100644
--- a/2018/12/18/lowprecision-conv.html
+++ b/2018/12/18/lowprecision-conv.html
@@ -126,9 +126,9 @@
- <li> <a href="https://sampl.cs.washington.edu/tvmconf">TVM Conference</a></li>
+ <li> <a href="https://tvmconf.org">TVM Conference</a></li>
<li> <a href="https://docs.tvm.ai">Docs</a></li>
- <li> <a href="https://github.com/dmlc/tvm/">Github</a></li>
+ <li> <a href="https://github.com/apache/incubator-tvm/">Github</a></li>
<li> <a href="/asf">ASF</a></li>
</ul>
</div>
@@ -141,7 +141,7 @@
<div class="span14">
<h1>Automating Generation of Low Precision Deep Learning Operators </h1>
<p class="post-meta">
- <time datetime="2018-12-18T00:00:00-06:00" itemprop="datePublished">
+ <time datetime="2018-12-18T00:00:00-08:00" itemprop="datePublished">
Dec 18, 2018
</time>
diff --git a/2019/01/19/Golang.html b/2019/01/19/Golang.html
index 61905d1..5a58afa 100644
--- a/2019/01/19/Golang.html
+++ b/2019/01/19/Golang.html
@@ -126,9 +126,9 @@
- <li> <a href="https://sampl.cs.washington.edu/tvmconf">TVM Conference</a></li>
+ <li> <a href="https://tvmconf.org">TVM Conference</a></li>
<li> <a href="https://docs.tvm.ai">Docs</a></li>
- <li> <a href="https://github.com/dmlc/tvm/">Github</a></li>
+ <li> <a href="https://github.com/apache/incubator-tvm/">Github</a></li>
<li> <a href="/asf">ASF</a></li>
</ul>
</div>
@@ -141,7 +141,7 @@
<div class="span14">
<h1>TVM Golang Runtime for Deep Learning Deployment </h1>
<p class="post-meta">
- <time datetime="2019-01-19T00:00:00-06:00" itemprop="datePublished">
+ <time datetime="2019-01-19T00:00:00-08:00" itemprop="datePublished">
Jan 19, 2019
</time>
diff --git a/2019/03/18/tvm-apache-announcement.html b/2019/03/18/tvm-apache-announcement.html
index 66bcf0a..e57f778 100644
--- a/2019/03/18/tvm-apache-announcement.html
+++ b/2019/03/18/tvm-apache-announcement.html
@@ -126,9 +126,9 @@
- <li> <a href="https://sampl.cs.washington.edu/tvmconf">TVM Conference</a></li>
+ <li> <a href="https://tvmconf.org">TVM Conference</a></li>
<li> <a href="https://docs.tvm.ai">Docs</a></li>
- <li> <a href="https://github.com/dmlc/tvm/">Github</a></li>
+ <li> <a href="https://github.com/apache/incubator-tvm/">Github</a></li>
<li> <a href="/asf">ASF</a></li>
</ul>
</div>
@@ -141,7 +141,7 @@
<div class="span14">
<h1>TVM Deep Learning Compiler Joins Apache Software Foundation </h1>
<p class="post-meta">
- <time datetime="2019-03-18T00:00:00-05:00" itemprop="datePublished">
+ <time datetime="2019-03-18T00:00:00-07:00" itemprop="datePublished">
Mar 18, 2019
</time>
diff --git a/2019/04/29/opt-cuda-quantized.html b/2019/04/29/opt-cuda-quantized.html
index 83b4846..f80befb 100644
--- a/2019/04/29/opt-cuda-quantized.html
+++ b/2019/04/29/opt-cuda-quantized.html
@@ -126,9 +126,9 @@
- <li> <a href="https://sampl.cs.washington.edu/tvmconf">TVM Conference</a></li>
+ <li> <a href="https://tvmconf.org">TVM Conference</a></li>
<li> <a href="https://docs.tvm.ai">Docs</a></li>
- <li> <a href="https://github.com/dmlc/tvm/">Github</a></li>
+ <li> <a href="https://github.com/apache/incubator-tvm/">Github</a></li>
<li> <a href="/asf">ASF</a></li>
</ul>
</div>
@@ -141,7 +141,7 @@
<div class="span14">
<h1>Automating Optimization of Quantized Deep Learning Models on CUDA </h1>
<p class="post-meta">
- <time datetime="2019-04-29T11:00:00-05:00" itemprop="datePublished">
+ <time datetime="2019-04-29T09:00:00-07:00" itemprop="datePublished">
Apr 29, 2019
</time>
diff --git a/2019/05/30/pytorch-frontend.html b/2019/05/30/pytorch-frontend.html
index a022294..46376ca 100644
--- a/2019/05/30/pytorch-frontend.html
+++ b/2019/05/30/pytorch-frontend.html
@@ -126,9 +126,9 @@
- <li> <a href="https://sampl.cs.washington.edu/tvmconf">TVM Conference</a></li>
+ <li> <a href="https://tvmconf.org">TVM Conference</a></li>
<li> <a href="https://docs.tvm.ai">Docs</a></li>
- <li> <a href="https://github.com/dmlc/tvm/">Github</a></li>
+ <li> <a href="https://github.com/apache/incubator-tvm/">Github</a></li>
<li> <a href="/asf">ASF</a></li>
</ul>
</div>
@@ -141,7 +141,7 @@
<div class="span14">
<h1>Integrating TVM into PyTorch </h1>
<p class="post-meta">
- <time datetime="2019-05-30T00:00:00-05:00" itemprop="datePublished">
+ <time datetime="2019-05-30T00:00:00-07:00" itemprop="datePublished">
May 30, 2019
</time>
diff --git a/about.html b/about.html
index c0deb69..cd98cf6 100644
--- a/about.html
+++ b/about.html
@@ -126,9 +126,9 @@
- <li> <a href="https://sampl.cs.washington.edu/tvmconf">TVM Conference</a></li>
+ <li> <a href="https://tvmconf.org">TVM Conference</a></li>
<li> <a href="https://docs.tvm.ai">Docs</a></li>
- <li> <a href="https://github.com/dmlc/tvm/">Github</a></li>
+ <li> <a href="https://github.com/apache/incubator-tvm/">Github</a></li>
<li> <a href="/asf">ASF</a></li>
</ul>
</div>
diff --git a/asf.html b/asf.html
index ea752b2..b9aabe7 100644
--- a/asf.html
+++ b/asf.html
@@ -126,9 +126,9 @@
- <li> <a href="https://sampl.cs.washington.edu/tvmconf">TVM Conference</a></li>
+ <li> <a href="https://tvmconf.org">TVM Conference</a></li>
<li> <a href="https://docs.tvm.ai">Docs</a></li>
- <li> <a href="https://github.com/dmlc/tvm/">Github</a></li>
+ <li> <a href="https://github.com/apache/incubator-tvm/">Github</a></li>
<li> <a href="/asf">ASF</a></li>
</ul>
</div>
diff --git a/atom.xml b/atom.xml
index 3207c6d..7b827e9 100644
--- a/atom.xml
+++ b/atom.xml
@@ -4,7 +4,7 @@
<title>TVM</title>
<link href="https://tvm.apache.org" rel="self"/>
<link href="https://tvm.apache.org"/>
- <updated>2020-03-04T10:58:38-06:00</updated>
+ <updated>2020-03-19T20:36:17-07:00</updated>
<id>https://tvm.apache.org</id>
<author>
<name></name>
@@ -15,7 +15,7 @@
<entry>
<title>Integrating TVM into PyTorch</title>
<link href="https://tvm.apache.org/2019/05/30/pytorch-frontend"/>
- <updated>2019-05-30T00:00:00-05:00</updated>
+ <updated>2019-05-30T00:00:00-07:00</updated>
<id>https://tvm.apache.org/2019/05/30/pytorch-frontend</id>
<content type="html"><p>As TVM continuously demonstrates improvements to the efficiency of deep learning execution,
it has become clear that PyTorch stands to benefit from directly leveraging the compiler stack.
@@ -117,7 +117,7 @@ relay_graph = torch_tvm.to_relay(mul, inputs)
<entry>
<title>Automating Optimization of Quantized Deep Learning Models on CUDA</title>
<link href="https://tvm.apache.org/2019/04/29/opt-cuda-quantized"/>
- <updated>2019-04-29T11:00:00-05:00</updated>
+ <updated>2019-04-29T09:00:00-07:00</updated>
<id>https://tvm.apache.org/2019/04/29/opt-cuda-quantized</id>
<content type="html"><p>Deep learning has been successfully applied to a variety of tasks.
On real-time scenarios such as inference on autonomous vehicles, the inference speed of the model is critical.
@@ -261,7 +261,7 @@ We show that automatic optimization in TVM makes it easy and flexible to support
<entry>
<title>TVM Deep Learning Compiler Joins Apache Software Foundation</title>
<link href="https://tvm.apache.org/2019/03/18/tvm-apache-announcement"/>
- <updated>2019-03-18T00:00:00-05:00</updated>
+ <updated>2019-03-18T00:00:00-07:00</updated>
<id>https://tvm.apache.org/2019/03/18/tvm-apache-announcement</id>
<content type="html"><p>There is an increasing need to bring machine learning to a wide diversity of hardware devices. Current frameworks rely on vendor-specific operator libraries and optimize for a narrow range of server-class GPUs. Deploying workloads to new platforms – such as mobile phones, embedded devices, and accelerators (e.g., FPGAs, ASICs) – requires significant manual effort.</p>
@@ -284,7 +284,7 @@ We show that automatic optimization in TVM makes it easy and flexible to support
<entry>
<title>TVM Golang Runtime for Deep Learning Deployment</title>
<link href="https://tvm.apache.org/2019/01/19/Golang"/>
- <updated>2019-01-19T00:00:00-06:00</updated>
+ <updated>2019-01-19T00:00:00-08:00</updated>
<id>https://tvm.apache.org/2019/01/19/Golang</id>
<content type="html"><h2 id="introduction">Introduction</h2>
@@ -454,7 +454,7 @@ closure as TVM packed function and invoke the same across programming language b
<entry>
<title>Automating Generation of Low Precision Deep Learning Operators</title>
<link href="https://tvm.apache.org/2018/12/18/lowprecision-conv"/>
- <updated>2018-12-18T00:00:00-06:00</updated>
+ <updated>2018-12-18T00:00:00-08:00</updated>
<id>https://tvm.apache.org/2018/12/18/lowprecision-conv</id>
<content type="html"><p>As deep learning models grow larger and more complex, deploying them on low powered phone and IoT
devices becomes challenging because of their limited compute and energy budgets. A recent trend
@@ -615,7 +615,7 @@ Note: x86 doesn’t support a vectorized popcount for this microarchitecture, so
<entry>
<title>Efficient Privacy-Preserving ML Using TVM</title>
<link href="https://tvm.apache.org/2018/10/09/ml-in-tees"/>
- <updated>2018-10-09T00:00:00-05:00</updated>
+ <updated>2018-10-09T00:00:00-07:00</updated>
<id>https://tvm.apache.org/2018/10/09/ml-in-tees</id>
<content type="html"><p>This post describes Myelin, a framework for privacy-preserving machine learning in trusted hardware enclaves, and how TVM makes Myelin fast.
The key idea is that TVM, unlike other popular ML frameworks, compiles models into lightweight, optimized, and dependency-free libraries which can fit into resource constrained enclaves.</p>
@@ -731,7 +731,7 @@ His research interest is in the general domain of ML on shared private data, but
<entry>
<title>Automatic Kernel Optimization for Deep Learning on All Hardware Platforms</title>
<link href="https://tvm.apache.org/2018/10/03/auto-opt-all"/>
- <updated>2018-10-03T00:00:00-05:00</updated>
+ <updated>2018-10-03T00:00:00-07:00</updated>
<id>https://tvm.apache.org/2018/10/03/auto-opt-all</id>
<content type="html"><p>Optimizing the performance of deep neural network on a diverse range of hardware platforms is still a hard
problem for AI developers. In terms of system support, we are facing a many-to-many problem here:
@@ -1125,7 +1125,7 @@ for inference deployment. TVM just provides such a solution.</p>
<entry>
<title>Building a Cross-Framework Deep Learning Compiler via DLPack</title>
<link href="https://tvm.apache.org/2018/08/10/DLPack-Bridge"/>
- <updated>2018-08-10T00:00:00-05:00</updated>
+ <updated>2018-08-10T00:00:00-07:00</updated>
<id>https://tvm.apache.org/2018/08/10/DLPack-Bridge</id>
<content type="html"><p>Deep learning frameworks such as Tensorflow, PyTorch, and ApacheMxNet provide a
powerful toolbox for quickly prototyping and deploying deep learning models.
@@ -1264,7 +1264,7 @@ support, and can be used to implement convenient converters, such as
<entry>
<title>VTA: An Open, Customizable Deep Learning Acceleration Stack </title>
<link href="https://tvm.apache.org/2018/07/12/vta-release-announcement"/>
- <updated>2018-07-12T00:00:00-05:00</updated>
+ <updated>2018-07-12T00:00:00-07:00</updated>
<id>https://tvm.apache.org/2018/07/12/vta-release-announcement</id>
<content type="html"><p style="text-align: center">Thierry Moreau(VTA architect), Tianqi Chen(TVM stack), Ziheng Jiang†(graph compilation), Luis Vega(cloud deployment)</p>
<p style="text-align: center">Advisors: Luis Ceze, Carlos Guestrin, Arvind Krishnamurthy</p>
@@ -1406,7 +1406,7 @@ This kind of high-level visibility is essential to system designers who want to
<entry>
<title>Bringing TVM into TensorFlow for Optimizing Neural Machine Translation on GPU</title>
<link href="https://tvm.apache.org/2018/03/23/nmt-transformer-optimize"/>
- <updated>2018-03-23T00:00:00-05:00</updated>
+ <updated>2018-03-23T00:00:00-07:00</updated>
<id>https://tvm.apache.org/2018/03/23/nmt-transformer-optimize</id>
<content type="html"><h2 id="author">Author</h2>
@@ -1672,7 +1672,7 @@ C = tvm.compute(
<entry>
<title>Compiling Deep Learning Models to WebGL with TVM</title>
<link href="https://tvm.apache.org/2018/03/12/webgl"/>
- <updated>2018-03-12T00:00:00-05:00</updated>
+ <updated>2018-03-12T00:00:00-07:00</updated>
<id>https://tvm.apache.org/2018/03/12/webgl</id>
<content type="html"><p>Now TVM comes with a brand-new OpenGL/WebGL backend!
This blog post explains what it is, and what you can achieve with it.</p>
@@ -1788,7 +1788,7 @@ optimizations into the TVM stack.</p>
<entry>
<title>Optimizing Mobile Deep Learning on ARM GPU with TVM</title>
<link href="https://tvm.apache.org/2018/01/16/opt-mali-gpu"/>
- <updated>2018-01-16T00:00:00-06:00</updated>
+ <updated>2018-01-16T00:00:00-08:00</updated>
<id>https://tvm.apache.org/2018/01/16/opt-mali-gpu</id>
<content type="html"><p>With the great success of deep learning, the demand for
deploying deep neural networks to mobile devices is growing rapidly.
@@ -2362,7 +2362,7 @@ advice and <a href="https://github.com/yzhliu">Yizhi Liu</a&g
<entry>
<title>Remote Profile and Test Deep Learning Cross Compilation on Mobile Phones with TVM RPC</title>
<link href="https://tvm.apache.org/2017/11/08/android-rpc-introduction"/>
- <updated>2017-11-08T00:00:00-06:00</updated>
+ <updated>2017-11-08T00:00:00-08:00</updated>
<id>https://tvm.apache.org/2017/11/08/android-rpc-introduction</id>
<content type="html"><p>TVM stack is an end to end compilation stack to deploy deep learning workloads to all hardware backends.
Thanks to the NNVM compiler support of TVM stack, we can now directly compile descriptions from deep learning frameworks and compile them to bare metal code.
@@ -2590,7 +2590,7 @@ make jvminstall
<entry>
<title>Bringing AMDGPUs to TVM Stack and NNVM Compiler with ROCm</title>
<link href="https://tvm.apache.org/2017/10/30/Bringing-AMDGPUs-to-TVM-Stack-and-NNVM-Compiler-with-ROCm"/>
- <updated>2017-10-30T00:00:00-05:00</updated>
+ <updated>2017-10-30T00:00:00-07:00</updated>
<id>https://tvm.apache.org/2017/10/30/Bringing-AMDGPUs-to-TVM-Stack-and-NNVM-Compiler-with-ROCm</id>
<content type="html"><p style="text-align: center">Aditya Atluri, Advanced Micro Devices, Inc.</p>
<p style="text-align: center">Masahiro Masuda, Ziosoft, Inc.</p>
@@ -2816,7 +2816,7 @@ BB0_6:
<entry>
<title>NNVM Compiler: Open Compiler for AI Frameworks</title>
<link href="https://tvm.apache.org/2017/10/06/nnvm-compiler-announcement"/>
- <updated>2017-10-06T10:30:00-05:00</updated>
+ <updated>2017-10-06T08:30:00-07:00</updated>
<id>https://tvm.apache.org/2017/10/06/nnvm-compiler-announcement</id>
<content type="html"><p style="text-align: center">Paul G. Allen School of Computer Science &amp; Engineering, University of Washington</p>
<p style="text-align: center">Amazon Web Service AI team</p>
@@ -2899,7 +2899,7 @@ We also learns from Halide when implementing the lowering pipeline in TVM.</l
<entry>
<title>Optimize Deep Learning GPU Operators with TVM: A Depthwise Convolution Example</title>
<link href="https://tvm.apache.org/2017/08/22/Optimize-Deep-Learning-GPU-Operators-with-TVM-A-Depthwise-Convolution-Example"/>
- <updated>2017-08-22T00:00:00-05:00</updated>
+ <updated>2017-08-22T00:00:00-07:00</updated>
<id>https://tvm.apache.org/2017/08/22/Optimize-Deep-Learning-GPU-Operators-with-TVM-A-Depthwise-Convolution-Example</id>
<content type="html"><p>Efficient deep learning operators are at the core of deep learning systems.
Usually these operators are hard to optimize and require great efforts of HPC experts.
@@ -3478,7 +3478,7 @@ He is experiencing a gap year after obtaining a bachelor’s degree in electrica
<entry>
<title>TVM: An End to End IR Stack for Deploying Deep Learning Workloads on Hardware Platforms</title>
<link href="https://tvm.apache.org/2017/08/17/tvm-release-announcement"/>
- <updated>2017-08-17T14:00:00-05:00</updated>
+ <updated>2017-08-17T12:00:00-07:00</updated>
<id>https://tvm.apache.org/2017/08/17/tvm-release-announcement</id>
<content type="html"><p style="text-align: center">Tianqi Chen(project lead), Thierry Moreau(hardware stack), Ziheng Jiang†(graph compilation), Haichen Shen(gpu optimization)</p>
<p style="text-align: center">Advisors: Luis Ceze, Carlos Guestrin, Arvind Krishnamurthy</p>
diff --git a/blog.html b/blog.html
index 5f2878a..7bca7df 100644
--- a/blog.html
+++ b/blog.html
@@ -126,9 +126,9 @@
- <li> <a href="https://sampl.cs.washington.edu/tvmconf">TVM Conference</a></li>
+ <li> <a href="https://tvmconf.org">TVM Conference</a></li>
<li> <a href="https://docs.tvm.ai">Docs</a></li>
- <li> <a href="https://github.com/dmlc/tvm/">Github</a></li>
+ <li> <a href="https://github.com/apache/incubator-tvm/">Github</a></li>
<li> <a href="/asf">ASF</a></li>
</ul>
</div>
diff --git a/categories.html b/categories.html
index 0ad1d6d..1b49615 100644
--- a/categories.html
+++ b/categories.html
@@ -126,9 +126,9 @@
- <li> <a href="https://sampl.cs.washington.edu/tvmconf">TVM Conference</a></li>
+ <li> <a href="https://tvmconf.org">TVM Conference</a></li>
<li> <a href="https://docs.tvm.ai">Docs</a></li>
- <li> <a href="https://github.com/dmlc/tvm/">Github</a></li>
+ <li> <a href="https://github.com/apache/incubator-tvm/">Github</a></li>
<li> <a href="/asf">ASF</a></li>
</ul>
</div>
diff --git a/community.html b/community.html
index c7fa420..4a6ed3c 100644
--- a/community.html
+++ b/community.html
@@ -126,9 +126,9 @@
- <li> <a href="https://sampl.cs.washington.edu/tvmconf">TVM Conference</a></li>
+ <li> <a href="https://tvmconf.org">TVM Conference</a></li>
<li> <a href="https://docs.tvm.ai">Docs</a></li>
- <li> <a href="https://github.com/dmlc/tvm/">Github</a></li>
+ <li> <a href="https://github.com/apache/incubator-tvm/">Github</a></li>
<li> <a href="/asf">ASF</a></li>
</ul>
</div>
diff --git a/index.html b/index.html
index 57b039f..c4d1481 100644
--- a/index.html
+++ b/index.html
@@ -126,9 +126,9 @@
- <li> <a href="https://sampl.cs.washington.edu/tvmconf">TVM Conference</a></li>
+ <li> <a href="https://tvmconf.org">TVM Conference</a></li>
<li> <a href="https://docs.tvm.ai">Docs</a></li>
- <li> <a href="https://github.com/dmlc/tvm/">Github</a></li>
+ <li> <a href="https://github.com/apache/incubator-tvm/">Github</a></li>
<li> <a href="/asf">ASF</a></li>
</ul>
</div>
diff --git a/rss.xml b/rss.xml
index f620099..d878f21 100644
--- a/rss.xml
+++ b/rss.xml
@@ -5,8 +5,8 @@
<description>TVM - </description>
<link>https://tvm.apache.org</link>
<atom:link href="https://tvm.apache.org" rel="self" type="application/rss+xml" />
- <lastBuildDate>Wed, 04 Mar 2020 10:58:38 -0600</lastBuildDate>
- <pubDate>Wed, 04 Mar 2020 10:58:38 -0600</pubDate>
+ <lastBuildDate>Thu, 19 Mar 2020 20:36:17 -0700</lastBuildDate>
+ <pubDate>Thu, 19 Mar 2020 20:36:17 -0700</pubDate>
<ttl>60</ttl>
@@ -109,7 +109,7 @@ relay_graph = torch_tvm.to_relay(mul, inputs)
</description>
<link>https://tvm.apache.org/2019/05/30/pytorch-frontend</link>
<guid>https://tvm.apache.org/2019/05/30/pytorch-frontend</guid>
- <pubDate>Thu, 30 May 2019 00:00:00 -0500</pubDate>
+ <pubDate>Thu, 30 May 2019 00:00:00 -0700</pubDate>
</item>
<item>
@@ -253,7 +253,7 @@ We show that automatic optimization in TVM makes it easy and flexible to support
</description>
<link>https://tvm.apache.org/2019/04/29/opt-cuda-quantized</link>
<guid>https://tvm.apache.org/2019/04/29/opt-cuda-quantized</guid>
- <pubDate>Mon, 29 Apr 2019 11:00:00 -0500</pubDate>
+ <pubDate>Mon, 29 Apr 2019 09:00:00 -0700</pubDate>
</item>
<item>
@@ -276,7 +276,7 @@ We show that automatic optimization in TVM makes it easy and flexible to support
</description>
<link>https://tvm.apache.org/2019/03/18/tvm-apache-announcement</link>
<guid>https://tvm.apache.org/2019/03/18/tvm-apache-announcement</guid>
- <pubDate>Mon, 18 Mar 2019 00:00:00 -0500</pubDate>
+ <pubDate>Mon, 18 Mar 2019 00:00:00 -0700</pubDate>
</item>
<item>
@@ -446,7 +446,7 @@ closure as TVM packed function and invoke the same across programming language b
</description>
<link>https://tvm.apache.org/2019/01/19/Golang</link>
<guid>https://tvm.apache.org/2019/01/19/Golang</guid>
- <pubDate>Sat, 19 Jan 2019 00:00:00 -0600</pubDate>
+ <pubDate>Sat, 19 Jan 2019 00:00:00 -0800</pubDate>
</item>
<item>
@@ -607,7 +607,7 @@ Note: x86 doesn’t support a vectorized popcount for this microarchitecture, so
</description>
<link>https://tvm.apache.org/2018/12/18/lowprecision-conv</link>
<guid>https://tvm.apache.org/2018/12/18/lowprecision-conv</guid>
- <pubDate>Tue, 18 Dec 2018 00:00:00 -0600</pubDate>
+ <pubDate>Tue, 18 Dec 2018 00:00:00 -0800</pubDate>
</item>
<item>
@@ -723,7 +723,7 @@ His research interest is in the general domain of ML on shared private data, but
</description>
<link>https://tvm.apache.org/2018/10/09/ml-in-tees</link>
<guid>https://tvm.apache.org/2018/10/09/ml-in-tees</guid>
- <pubDate>Tue, 09 Oct 2018 00:00:00 -0500</pubDate>
+ <pubDate>Tue, 09 Oct 2018 00:00:00 -0700</pubDate>
</item>
<item>
@@ -1117,7 +1117,7 @@ for inference deployment. TVM just provides such a solution.</p>
</description>
<link>https://tvm.apache.org/2018/10/03/auto-opt-all</link>
<guid>https://tvm.apache.org/2018/10/03/auto-opt-all</guid>
- <pubDate>Wed, 03 Oct 2018 00:00:00 -0500</pubDate>
+ <pubDate>Wed, 03 Oct 2018 00:00:00 -0700</pubDate>
</item>
<item>
@@ -1256,7 +1256,7 @@ support, and can be used to implement convenient converters, such as
</description>
<link>https://tvm.apache.org/2018/08/10/DLPack-Bridge</link>
<guid>https://tvm.apache.org/2018/08/10/DLPack-Bridge</guid>
- <pubDate>Fri, 10 Aug 2018 00:00:00 -0500</pubDate>
+ <pubDate>Fri, 10 Aug 2018 00:00:00 -0700</pubDate>
</item>
<item>
@@ -1398,7 +1398,7 @@ This kind of high-level visibility is essential to system designers who want to
</description>
<link>https://tvm.apache.org/2018/07/12/vta-release-announcement</link>
<guid>https://tvm.apache.org/2018/07/12/vta-release-announcement</guid>
- <pubDate>Thu, 12 Jul 2018 00:00:00 -0500</pubDate>
+ <pubDate>Thu, 12 Jul 2018 00:00:00 -0700</pubDate>
</item>
<item>
@@ -1664,7 +1664,7 @@ C = tvm.compute(
</description>
<link>https://tvm.apache.org/2018/03/23/nmt-transformer-optimize</link>
<guid>https://tvm.apache.org/2018/03/23/nmt-transformer-optimize</guid>
- <pubDate>Fri, 23 Mar 2018 00:00:00 -0500</pubDate>
+ <pubDate>Fri, 23 Mar 2018 00:00:00 -0700</pubDate>
</item>
<item>
@@ -1780,7 +1780,7 @@ optimizations into the TVM stack.</p>
</description>
<link>https://tvm.apache.org/2018/03/12/webgl</link>
<guid>https://tvm.apache.org/2018/03/12/webgl</guid>
- <pubDate>Mon, 12 Mar 2018 00:00:00 -0500</pubDate>
+ <pubDate>Mon, 12 Mar 2018 00:00:00 -0700</pubDate>
</item>
<item>
@@ -2354,7 +2354,7 @@ advice and <a href="https://github.com/yzhliu">Yizhi Liu</a&g
</description>
<link>https://tvm.apache.org/2018/01/16/opt-mali-gpu</link>
<guid>https://tvm.apache.org/2018/01/16/opt-mali-gpu</guid>
- <pubDate>Tue, 16 Jan 2018 00:00:00 -0600</pubDate>
+ <pubDate>Tue, 16 Jan 2018 00:00:00 -0800</pubDate>
</item>
<item>
@@ -2582,7 +2582,7 @@ make jvminstall
</description>
<link>https://tvm.apache.org/2017/11/08/android-rpc-introduction</link>
<guid>https://tvm.apache.org/2017/11/08/android-rpc-introduction</guid>
- <pubDate>Wed, 08 Nov 2017 00:00:00 -0600</pubDate>
+ <pubDate>Wed, 08 Nov 2017 00:00:00 -0800</pubDate>
</item>
<item>
@@ -2808,7 +2808,7 @@ BB0_6:
</description>
<link>https://tvm.apache.org/2017/10/30/Bringing-AMDGPUs-to-TVM-Stack-and-NNVM-Compiler-with-ROCm</link>
<guid>https://tvm.apache.org/2017/10/30/Bringing-AMDGPUs-to-TVM-Stack-and-NNVM-Compiler-with-ROCm</guid>
- <pubDate>Mon, 30 Oct 2017 00:00:00 -0500</pubDate>
+ <pubDate>Mon, 30 Oct 2017 00:00:00 -0700</pubDate>
</item>
<item>
@@ -2891,7 +2891,7 @@ We also learns from Halide when implementing the lowering pipeline in TVM.</l
</description>
<link>https://tvm.apache.org/2017/10/06/nnvm-compiler-announcement</link>
<guid>https://tvm.apache.org/2017/10/06/nnvm-compiler-announcement</guid>
- <pubDate>Fri, 06 Oct 2017 10:30:00 -0500</pubDate>
+ <pubDate>Fri, 06 Oct 2017 08:30:00 -0700</pubDate>
</item>
<item>
@@ -3470,7 +3470,7 @@ He is experiencing a gap year after obtaining a bachelor’s degree in electrica
</description>
<link>https://tvm.apache.org/2017/08/22/Optimize-Deep-Learning-GPU-Operators-with-TVM-A-Depthwise-Convolution-Example</link>
<guid>https://tvm.apache.org/2017/08/22/Optimize-Deep-Learning-GPU-Operators-with-TVM-A-Depthwise-Convolution-Example</guid>
- <pubDate>Tue, 22 Aug 2017 00:00:00 -0500</pubDate>
+ <pubDate>Tue, 22 Aug 2017 00:00:00 -0700</pubDate>
</item>
<item>
@@ -3598,7 +3598,7 @@ that adopts the standard, such as MXNet, PyTorch, Caffe2 and tiny-dnn.</li>
</description>
<link>https://tvm.apache.org/2017/08/17/tvm-release-announcement</link>
<guid>https://tvm.apache.org/2017/08/17/tvm-release-announcement</guid>
- <pubDate>Thu, 17 Aug 2017 14:00:00 -0500</pubDate>
+ <pubDate>Thu, 17 Aug 2017 12:00:00 -0700</pubDate>
</item>
diff --git a/tags.html b/tags.html
index c068e9b..a0c200f 100644
--- a/tags.html
+++ b/tags.html
@@ -126,9 +126,9 @@
- <li> <a href="https://sampl.cs.washington.edu/tvmconf">TVM Conference</a></li>
+ <li> <a href="https://tvmconf.org">TVM Conference</a></li>
<li> <a href="https://docs.tvm.ai">Docs</a></li>
- <li> <a href="https://github.com/dmlc/tvm/">Github</a></li>
+ <li> <a href="https://github.com/apache/incubator-tvm/">Github</a></li>
<li> <a href="/asf">ASF</a></li>
</ul>
</div>
diff --git a/vta.html b/vta.html
index f3ef3c7..a84b76c 100644
--- a/vta.html
+++ b/vta.html
@@ -126,9 +126,9 @@
- <li> <a href="https://sampl.cs.washington.edu/tvmconf">TVM Conference</a></li>
+ <li> <a href="https://tvmconf.org">TVM Conference</a></li>
<li> <a href="https://docs.tvm.ai">Docs</a></li>
- <li> <a href="https://github.com/dmlc/tvm/">Github</a></li>
+ <li> <a href="https://github.com/apache/incubator-tvm/">Github</a></li>
<li> <a href="/asf">ASF</a></li>
</ul>
</div>