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Posted to commits@tvm.apache.org by tq...@apache.org on 2020/10/02 18:07:22 UTC
[incubator-tvm-site] branch asf-site updated: Build at Fri Oct 2
14:07:12 EDT 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 0428c72 Build at Fri Oct 2 14:07:12 EDT 2020
0428c72 is described below
commit 0428c72b6c966dd575d7b0d2df3473123afa36ef
Author: tqchen <tq...@gmail.com>
AuthorDate: Fri Oct 2 14:07:12 2020 -0400
Build at Fri Oct 2 14:07:12 EDT 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 +--
...machine-learning-to-webassembly-and-webgpu.html | 6 +--
2020/05/20/bring-your-own-datatypes.html | 6 +--
2020/06/04/tinyml-how-tvm-is-taming-tiny.html | 6 +--
2020/07/14/bert-pytorch-tvm.html | 6 +--
.../15/how-to-bring-your-own-codegen-to-tvm.html | 6 +--
asf.html | 4 +-
atom.xml | 42 ++++++++++-----------
blog.html | 4 +-
categories.html | 4 +-
download.html | 4 +-
feed.xml | 20 +++++-----
history.html | 4 +-
index.html | 4 +-
rss.xml | 44 +++++++++++-----------
tags.html | 4 +-
vta.html | 4 +-
33 files changed, 135 insertions(+), 135 deletions(-)
diff --git a/2017/08/17/tvm-release-announcement.html b/2017/08/17/tvm-release-announcement.html
index 60deb64..6e0ad90 100644
--- a/2017/08/17/tvm-release-announcement.html
+++ b/2017/08/17/tvm-release-announcement.html
@@ -80,7 +80,7 @@
</li>
<li>
- <a href="https://apachecon.com/?ref=tvm.apache.org">Current Events</a>
+ <a href="https://www.apache.org/events/">Current Events</a>
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@@ -120,7 +120,7 @@
</li>
<li>
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+ <a href="https://www.apache.org/events/">Current Events</a>
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@@ -140,7 +140,7 @@
<div class="span14">
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<p class="post-meta">
- <time datetime="2017-08-17T12:00:00-07:00" itemprop="datePublished">
+ <time datetime="2017-08-17T15:00:00-04:00" 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 b1408bd..3ce7269 100644
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+++ b/2017/08/22/Optimize-Deep-Learning-GPU-Operators-with-TVM-A-Depthwise-Convolution-Example.html
@@ -80,7 +80,7 @@
</li>
<li>
- <a href="https://apachecon.com/?ref=tvm.apache.org">Current Events</a>
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@@ -140,7 +140,7 @@
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<h1>Optimize Deep Learning GPU Operators with TVM: A Depthwise Convolution Example </h1>
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- <time datetime="2017-08-22T00:00:00-07:00" itemprop="datePublished">
+ <time datetime="2017-08-22T00:00:00-04:00" 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 25deba8..1b5e3cf 100644
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@@ -80,7 +80,7 @@
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@@ -140,7 +140,7 @@
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<h1>NNVM Compiler: Open Compiler for AI Frameworks </h1>
<p class="post-meta">
- <time datetime="2017-10-06T08:30:00-07:00" itemprop="datePublished">
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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 19898e0..77923a5 100644
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<h1>Bringing AMDGPUs to TVM Stack and NNVM Compiler with ROCm </h1>
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- <time datetime="2017-10-30T00:00:00-07:00" itemprop="datePublished">
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Oct 30, 2017
</time>
diff --git a/2017/11/08/android-rpc-introduction.html b/2017/11/08/android-rpc-introduction.html
index 48d6e04..3047e65 100644
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@@ -80,7 +80,7 @@
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@@ -120,7 +120,7 @@
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@@ -140,7 +140,7 @@
<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="2017-11-08T00:00:00-08:00" itemprop="datePublished">
+ <time datetime="2017-11-08T00:00:00-05:00" 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 6f49e10..51b9423 100644
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@@ -80,7 +80,7 @@
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<h1>Optimizing Mobile Deep Learning on ARM GPU with TVM </h1>
<p class="post-meta">
- <time datetime="2018-01-16T00:00:00-08:00" itemprop="datePublished">
+ <time datetime="2018-01-16T00:00:00-05:00" itemprop="datePublished">
Jan 16, 2018
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diff --git a/2018/03/12/webgl.html b/2018/03/12/webgl.html
index f24a5c0..5f67d3f 100644
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+++ b/2018/03/12/webgl.html
@@ -80,7 +80,7 @@
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@@ -140,7 +140,7 @@
<div class="span14">
<h1>Compiling Deep Learning Models to WebGL with TVM </h1>
<p class="post-meta">
- <time datetime="2018-03-12T00:00:00-07:00" itemprop="datePublished">
+ <time datetime="2018-03-12T00:00:00-04:00" 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 3a97c52..a71de61 100644
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@@ -80,7 +80,7 @@
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@@ -140,7 +140,7 @@
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<h1>Bringing TVM into TensorFlow for Optimizing Neural Machine Translation on GPU </h1>
<p class="post-meta">
- <time datetime="2018-03-23T00:00:00-07:00" itemprop="datePublished">
+ <time datetime="2018-03-23T00:00:00-04:00" itemprop="datePublished">
Mar 23, 2018
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diff --git a/2018/07/12/vta-release-announcement.html b/2018/07/12/vta-release-announcement.html
index 9cf8ecc..86f99a5 100644
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@@ -80,7 +80,7 @@
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@@ -140,7 +140,7 @@
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<h1>VTA: An Open, Customizable Deep Learning Acceleration Stack </h1>
<p class="post-meta">
- <time datetime="2018-07-12T00:00:00-07:00" itemprop="datePublished">
+ <time datetime="2018-07-12T00:00:00-04:00" itemprop="datePublished">
Jul 12, 2018
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diff --git a/2018/08/10/DLPack-Bridge.html b/2018/08/10/DLPack-Bridge.html
index 949cbd1..64cdcf2 100644
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@@ -80,7 +80,7 @@
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<div class="span14">
<h1>Building a Cross-Framework Deep Learning Compiler via DLPack </h1>
<p class="post-meta">
- <time datetime="2018-08-10T00:00:00-07:00" itemprop="datePublished">
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Aug 10, 2018
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diff --git a/2018/10/03/auto-opt-all.html b/2018/10/03/auto-opt-all.html
index 3ad2052..750ee89 100644
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- <a href="https://apachecon.com/?ref=tvm.apache.org">Current Events</a>
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@@ -140,7 +140,7 @@
<div class="span14">
<h1>Automatic Kernel Optimization for Deep Learning on All Hardware Platforms </h1>
<p class="post-meta">
- <time datetime="2018-10-03T00:00:00-07:00" itemprop="datePublished">
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Oct 3, 2018
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diff --git a/2018/10/09/ml-in-tees.html b/2018/10/09/ml-in-tees.html
index d85dec6..83f024f 100644
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<div class="span14">
<h1>Efficient Privacy-Preserving ML Using TVM </h1>
<p class="post-meta">
- <time datetime="2018-10-09T00:00:00-07:00" itemprop="datePublished">
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Oct 9, 2018
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diff --git a/2018/12/18/lowprecision-conv.html b/2018/12/18/lowprecision-conv.html
index 65a9bb1..f506d32 100644
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<h1>Automating Generation of Low Precision Deep Learning Operators </h1>
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- <time datetime="2018-12-18T00:00:00-08:00" itemprop="datePublished">
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Dec 18, 2018
</time>
diff --git a/2019/01/19/Golang.html b/2019/01/19/Golang.html
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@@ -80,7 +80,7 @@
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<li>
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+ <a href="https://www.apache.org/events/">Current Events</a>
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@@ -140,7 +140,7 @@
<div class="span14">
<h1>TVM Golang Runtime for Deep Learning Deployment </h1>
<p class="post-meta">
- <time datetime="2019-01-19T00:00:00-08:00" itemprop="datePublished">
+ <time datetime="2019-01-19T00:00:00-05:00" itemprop="datePublished">
Jan 19, 2019
</time>
diff --git a/2019/03/18/tvm-apache-announcement.html b/2019/03/18/tvm-apache-announcement.html
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<li>
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<div class="span14">
<h1>TVM Deep Learning Compiler Joins Apache Software Foundation </h1>
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Mar 18, 2019
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diff --git a/2019/04/29/opt-cuda-quantized.html b/2019/04/29/opt-cuda-quantized.html
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<li>
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<div class="span14">
<h1>Automating Optimization of Quantized Deep Learning Models on CUDA </h1>
<p class="post-meta">
- <time datetime="2019-04-29T09:00:00-07:00" itemprop="datePublished">
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Apr 29, 2019
</time>
diff --git a/2019/05/30/pytorch-frontend.html b/2019/05/30/pytorch-frontend.html
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<li>
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<li>
- <a href="https://apachecon.com/?ref=tvm.apache.org">Current Events</a>
+ <a href="https://www.apache.org/events/">Current Events</a>
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<div class="span14">
<h1>Integrating TVM into PyTorch </h1>
<p class="post-meta">
- <time datetime="2019-05-30T00:00:00-07:00" itemprop="datePublished">
+ <time datetime="2019-05-30T00:00:00-04:00" itemprop="datePublished">
May 30, 2019
</time>
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index c79e54a..a76db67 100644
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</li>
<li>
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+ <a href="https://www.apache.org/events/">Current Events</a>
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@@ -120,7 +120,7 @@
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<li>
- <a href="https://apachecon.com/?ref=tvm.apache.org">Current Events</a>
+ <a href="https://www.apache.org/events/">Current Events</a>
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@@ -140,7 +140,7 @@
<div class="span14">
<h1>Compiling Machine Learning to WASM and WebGPU with Apache TVM </h1>
<p class="post-meta">
- <time datetime="2020-05-14T00:00:00-07:00" itemprop="datePublished">
+ <time datetime="2020-05-14T00:00:00-04:00" itemprop="datePublished">
May 14, 2020
</time>
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index c682c96..d7c3e39 100644
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</li>
<li>
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+ <a href="https://www.apache.org/events/">Current Events</a>
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</li>
<li>
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+ <a href="https://www.apache.org/events/">Current Events</a>
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@@ -140,7 +140,7 @@
<div class="span14">
<h1>Bring Your Own Datatypes: Enabling Custom Datatype Exploration in TVM </h1>
<p class="post-meta">
- <time datetime="2020-05-20T00:00:00-07:00" itemprop="datePublished">
+ <time datetime="2020-05-20T00:00:00-04:00" itemprop="datePublished">
May 20, 2020
</time>
diff --git a/2020/06/04/tinyml-how-tvm-is-taming-tiny.html b/2020/06/04/tinyml-how-tvm-is-taming-tiny.html
index 2ce9c5f..5920988 100644
--- a/2020/06/04/tinyml-how-tvm-is-taming-tiny.html
+++ b/2020/06/04/tinyml-how-tvm-is-taming-tiny.html
@@ -80,7 +80,7 @@
</li>
<li>
- <a href="https://apachecon.com/?ref=tvm.apache.org">Current Events</a>
+ <a href="https://www.apache.org/events/">Current Events</a>
</li>
</ul>
@@ -120,7 +120,7 @@
</li>
<li>
- <a href="https://apachecon.com/?ref=tvm.apache.org">Current Events</a>
+ <a href="https://www.apache.org/events/">Current Events</a>
</li>
</ul>
@@ -140,7 +140,7 @@
<div class="span14">
<h1>TinyML - How TVM is Taming Tiny </h1>
<p class="post-meta">
- <time datetime="2020-06-04T00:00:00-07:00" itemprop="datePublished">
+ <time datetime="2020-06-04T00:00:00-04:00" itemprop="datePublished">
Jun 4, 2020
</time>
diff --git a/2020/07/14/bert-pytorch-tvm.html b/2020/07/14/bert-pytorch-tvm.html
index 01cacc2..6fae7b0 100644
--- a/2020/07/14/bert-pytorch-tvm.html
+++ b/2020/07/14/bert-pytorch-tvm.html
@@ -80,7 +80,7 @@
</li>
<li>
- <a href="https://apachecon.com/?ref=tvm.apache.org">Current Events</a>
+ <a href="https://www.apache.org/events/">Current Events</a>
</li>
</ul>
@@ -120,7 +120,7 @@
</li>
<li>
- <a href="https://apachecon.com/?ref=tvm.apache.org">Current Events</a>
+ <a href="https://www.apache.org/events/">Current Events</a>
</li>
</ul>
@@ -140,7 +140,7 @@
<div class="span14">
<h1>Bridging PyTorch and TVM </h1>
<p class="post-meta">
- <time datetime="2020-07-14T00:00:00-07:00" itemprop="datePublished">
+ <time datetime="2020-07-14T00:00:00-04:00" itemprop="datePublished">
Jul 14, 2020
</time>
diff --git a/2020/07/15/how-to-bring-your-own-codegen-to-tvm.html b/2020/07/15/how-to-bring-your-own-codegen-to-tvm.html
index 1ca9a2e..c922350 100644
--- a/2020/07/15/how-to-bring-your-own-codegen-to-tvm.html
+++ b/2020/07/15/how-to-bring-your-own-codegen-to-tvm.html
@@ -80,7 +80,7 @@
</li>
<li>
- <a href="https://apachecon.com/?ref=tvm.apache.org">Current Events</a>
+ <a href="https://www.apache.org/events/">Current Events</a>
</li>
</ul>
@@ -120,7 +120,7 @@
</li>
<li>
- <a href="https://apachecon.com/?ref=tvm.apache.org">Current Events</a>
+ <a href="https://www.apache.org/events/">Current Events</a>
</li>
</ul>
@@ -140,7 +140,7 @@
<div class="span14">
<h1>How to Bring Your Own Codegen to TVM </h1>
<p class="post-meta">
- <time datetime="2020-07-15T00:00:00-07:00" itemprop="datePublished">
+ <time datetime="2020-07-15T00:00:00-04:00" itemprop="datePublished">
Jul 15, 2020
</time>
diff --git a/asf.html b/asf.html
index 9e2ded1..185f00c 100644
--- a/asf.html
+++ b/asf.html
@@ -80,7 +80,7 @@
</li>
<li>
- <a href="https://apachecon.com/?ref=tvm.apache.org">Current Events</a>
+ <a href="https://www.apache.org/events/">Current Events</a>
</li>
</ul>
@@ -120,7 +120,7 @@
</li>
<li>
- <a href="https://apachecon.com/?ref=tvm.apache.org">Current Events</a>
+ <a href="https://www.apache.org/events/">Current Events</a>
</li>
</ul>
diff --git a/atom.xml b/atom.xml
index f5ccc40..13399da 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>2020-09-27T17:05:42-07:00</updated>
+ <updated>2020-10-02T14:07:08-04:00</updated>
<id>https://tvm.apache.org</id>
<author>
<name></name>
@@ -15,7 +15,7 @@
<entry>
<title>How to Bring Your Own Codegen to TVM</title>
<link href="https://tvm.apache.org/2020/07/15/how-to-bring-your-own-codegen-to-tvm"/>
- <updated>2020-07-15T00:00:00-07:00</updated>
+ <updated>2020-07-15T00:00:00-04:00</updated>
<id>https://tvm.apache.org/2020/07/15/how-to-bring-your-own-codegen-to-tvm</id>
<content type="html"><p>To free data scientists from worrying about the performance when developing a new model, hardware backend providers (e.g., Intel, NVIDIA, ARM, etc) either provide kernel libraries such as cuBLAS or cuDNN with many commonly used deep learning kernels, or provide frameworks such as DNNL or TensorRT with a graph engine to let users describe their models in a certain way to achieve high performance. In addition, emerging deep learning accelerators also have t [...]
@@ -494,7 +494,7 @@ Figure 4: After Graph Partitioning.
<entry>
<title>Bridging PyTorch and TVM</title>
<link href="https://tvm.apache.org/2020/07/14/bert-pytorch-tvm"/>
- <updated>2020-07-14T00:00:00-07:00</updated>
+ <updated>2020-07-14T00:00:00-04:00</updated>
<id>https://tvm.apache.org/2020/07/14/bert-pytorch-tvm</id>
<content type="html">
<p>(A more code-heavy variant is crossposted on the more PyTorch affine <a href="https://lernapparat.de/transformers-pytorch-tvm/">Lernapparat</a>,
@@ -1017,7 +1017,7 @@ He is a PyTorch core developer and co-authored <a href="https://www.mann
<entry>
<title>TinyML - How TVM is Taming Tiny</title>
<link href="https://tvm.apache.org/2020/06/04/tinyml-how-tvm-is-taming-tiny"/>
- <updated>2020-06-04T00:00:00-07:00</updated>
+ <updated>2020-06-04T00:00:00-04:00</updated>
<id>https://tvm.apache.org/2020/06/04/tinyml-how-tvm-is-taming-tiny</id>
<content type="html">
<p><img src="/images/microtvm/logo.png" alt="microTVM logo" width="30%" /><br /></p>
@@ -1326,7 +1326,7 @@ Diagram from CMSIS-NN paper showing a 2x2 matrix multiplication microkernel</
<entry>
<title>Bring Your Own Datatypes: Enabling Custom Datatype Exploration in TVM</title>
<link href="https://tvm.apache.org/2020/05/20/bring-your-own-datatypes"/>
- <updated>2020-05-20T00:00:00-07:00</updated>
+ <updated>2020-05-20T00:00:00-04:00</updated>
<id>https://tvm.apache.org/2020/05/20/bring-your-own-datatypes</id>
<content type="html"><p>In this post, we describe the Bring Your Own Datatypes framework, which enables the use of custom datatypes within TVM.</p>
@@ -1612,7 +1612,7 @@ The Bring Your Own Datatypes framework
<entry>
<title>Compiling Machine Learning to WASM and WebGPU with Apache TVM</title>
<link href="https://tvm.apache.org/2020/05/14/compiling-machine-learning-to-webassembly-and-webgpu"/>
- <updated>2020-05-14T00:00:00-07:00</updated>
+ <updated>2020-05-14T00:00:00-04:00</updated>
<id>https://tvm.apache.org/2020/05/14/compiling-machine-learning-to-webassembly-and-webgpu</id>
<content type="html"><p><strong>TLDR</strong></p>
@@ -1699,7 +1699,7 @@ The Bring Your Own Datatypes framework
<entry>
<title>Integrating TVM into PyTorch</title>
<link href="https://tvm.apache.org/2019/05/30/pytorch-frontend"/>
- <updated>2019-05-30T00:00:00-07:00</updated>
+ <updated>2019-05-30T00:00:00-04:00</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.
@@ -1801,7 +1801,7 @@ 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>2019-04-29T09:00:00-07:00</updated>
+ <updated>2019-04-29T12:00:00-04:00</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.
@@ -1945,7 +1945,7 @@ 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>2019-03-18T00:00:00-07:00</updated>
+ <updated>2019-03-18T00:00:00-04:00</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>
@@ -1968,7 +1968,7 @@ 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>2019-01-19T00:00:00-08:00</updated>
+ <updated>2019-01-19T00:00:00-05:00</updated>
<id>https://tvm.apache.org/2019/01/19/Golang</id>
<content type="html"><h2 id="introduction">Introduction</h2>
@@ -2138,7 +2138,7 @@ 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>2018-12-18T00:00:00-08:00</updated>
+ <updated>2018-12-18T00:00:00-05:00</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
@@ -2299,7 +2299,7 @@ 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>2018-10-09T00:00:00-07:00</updated>
+ <updated>2018-10-09T00:00:00-04:00</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>
@@ -2415,7 +2415,7 @@ 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>2018-10-03T00:00:00-07:00</updated>
+ <updated>2018-10-03T00:00:00-04:00</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:
@@ -2809,7 +2809,7 @@ 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>2018-08-10T00:00:00-07:00</updated>
+ <updated>2018-08-10T00:00:00-04:00</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.
@@ -2948,7 +2948,7 @@ 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>2018-07-12T00:00:00-07:00</updated>
+ <updated>2018-07-12T00:00:00-04:00</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>
@@ -3090,7 +3090,7 @@ 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>2018-03-23T00:00:00-07:00</updated>
+ <updated>2018-03-23T00:00:00-04:00</updated>
<id>https://tvm.apache.org/2018/03/23/nmt-transformer-optimize</id>
<content type="html"><h2 id="author">Author</h2>
@@ -3356,7 +3356,7 @@ 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>2018-03-12T00:00:00-07:00</updated>
+ <updated>2018-03-12T00:00:00-04:00</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>
@@ -3472,7 +3472,7 @@ 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>2018-01-16T00:00:00-08:00</updated>
+ <updated>2018-01-16T00:00:00-05:00</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.
@@ -4046,7 +4046,7 @@ 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>2017-11-08T00:00:00-08:00</updated>
+ <updated>2017-11-08T00:00:00-05:00</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.
@@ -4274,7 +4274,7 @@ 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>2017-10-30T00:00:00-07:00</updated>
+ <updated>2017-10-30T00:00:00-04:00</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>
@@ -4500,7 +4500,7 @@ 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>2017-10-06T08:30:00-07:00</updated>
+ <updated>2017-10-06T11:30:00-04:00</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>
diff --git a/blog.html b/blog.html
index 0fd55e1..221dd10 100644
--- a/blog.html
+++ b/blog.html
@@ -80,7 +80,7 @@
</li>
<li>
- <a href="https://apachecon.com/?ref=tvm.apache.org">Current Events</a>
+ <a href="https://www.apache.org/events/">Current Events</a>
</li>
</ul>
@@ -120,7 +120,7 @@
</li>
<li>
- <a href="https://apachecon.com/?ref=tvm.apache.org">Current Events</a>
+ <a href="https://www.apache.org/events/">Current Events</a>
</li>
</ul>
diff --git a/categories.html b/categories.html
index dc41475..3419c60 100644
--- a/categories.html
+++ b/categories.html
@@ -80,7 +80,7 @@
</li>
<li>
- <a href="https://apachecon.com/?ref=tvm.apache.org">Current Events</a>
+ <a href="https://www.apache.org/events/">Current Events</a>
</li>
</ul>
@@ -120,7 +120,7 @@
</li>
<li>
- <a href="https://apachecon.com/?ref=tvm.apache.org">Current Events</a>
+ <a href="https://www.apache.org/events/">Current Events</a>
</li>
</ul>
diff --git a/download.html b/download.html
index fef00d9..ef1b45c 100644
--- a/download.html
+++ b/download.html
@@ -80,7 +80,7 @@
</li>
<li>
- <a href="https://apachecon.com/?ref=tvm.apache.org">Current Events</a>
+ <a href="https://www.apache.org/events/">Current Events</a>
</li>
</ul>
@@ -120,7 +120,7 @@
</li>
<li>
- <a href="https://apachecon.com/?ref=tvm.apache.org">Current Events</a>
+ <a href="https://www.apache.org/events/">Current Events</a>
</li>
</ul>
diff --git a/feed.xml b/feed.xml
index e0a91c9..4ef2075 100644
--- a/feed.xml
+++ b/feed.xml
@@ -1,4 +1,4 @@
-<?xml version="1.0" encoding="utf-8"?><feed xmlns="http://www.w3.org/2005/Atom" ><generator uri="https://jekyllrb.com/" version="4.1.1">Jekyll</generator><link href="/feed.xml" rel="self" type="application/atom+xml" /><link href="/" rel="alternate" type="text/html" /><updated>2020-09-27T17:05:42-07:00</updated><id>/feed.xml</id><title type="html">TVM</title><author><name>{"name"=>nil}</name></author><entry><title type="html">How to Bring Your Own Codegen to TVM</title><link [...]
+<?xml version="1.0" encoding="utf-8"?><feed xmlns="http://www.w3.org/2005/Atom" ><generator uri="https://jekyllrb.com/" version="4.1.1">Jekyll</generator><link href="/feed.xml" rel="self" type="application/atom+xml" /><link href="/" rel="alternate" type="text/html" /><updated>2020-10-02T14:07:08-04:00</updated><id>/feed.xml</id><title type="html">TVM</title><author><name>{"name"=>nil}</name></author><entry><title type="html">How to Bring Your Own Codegen to TVM</title><link [...]
<p>However, users have to learn a new programming interface when they attempt to work on a new kernel library or a device. As a result, the demand for a unified programming interface becomes more and more important to let all users and hardware backend providers stand on the same page.</p>
@@ -467,7 +467,7 @@ Figure 4: After Graph Partitioning.
<h2 id="acknowledgment">Acknowledgment</h2>
-<p>We would like to thank our colleague Animesh Jain for valuable discussions in the framework design; Tianqi Chen and Jared Roesch from OctoML for system design discussions and prototyping; Masahiro Masuda from the TVM community to help code review and improve the DNNL integration. We would also like to thank Ramana Radhakrishnan, Matthew Barrett, Manupa Karunaratne, and Luke Hutton from ARM, U.K. for contributing several helpful ideas, related Relay passes, and the Arm Compute Li [...]
+<p>We would like to thank our colleague Animesh Jain for valuable discussions in the framework design; Tianqi Chen and Jared Roesch from OctoML for system design discussions and prototyping; Masahiro Masuda from the TVM community to help code review and improve the DNNL integration. We would also like to thank Ramana Radhakrishnan, Matthew Barrett, Manupa Karunaratne, and Luke Hutton from ARM, U.K. for contributing several helpful ideas, related Relay passes, and the Arm Compute Li [...]
the Jupyter Notebook to follow along is on <a href="https://github.com/t-vi/pytorch-tvmisc/tree/master/transformers-pytorch-tvm/">github</a>.)</p>
<p>Some of the most intriguing applications of Artificial Intelligence have been in Natural Language Processing.
@@ -980,7 +980,7 @@ one would want to re-do cheap computation, most prominently point-wise computati
<h1 id="author">Author</h1>
<p><a href="https://lernapparat.de/">Thomas Viehmann</a> is the founder of <a href="https://mathinf.eu/">MathInf GmbH</a>, Munich, Germany, a boutique training and consultancy firm focusing on Machine Learning and PyTorch.
-He is a PyTorch core developer and co-authored <a href="https://www.manning.com/books/deep-learning-with-pytorch">Deep Learning with PyTorch</a>, which currently available as <a href="https://pytorch.org/deep-learning-with-pytorch">free download from the PyTorch website</a>.</p></content><author><name>Thomas Viehmann, MathInf GmbH</name></author><summary type="html"></summary></entry><entry><title type="html">TinyML - How TVM is Taming Ti [...]
+He is a PyTorch core developer and co-authored <a href="https://www.manning.com/books/deep-learning-with-pytorch">Deep Learning with PyTorch</a>, which currently available as <a href="https://pytorch.org/deep-learning-with-pytorch">free download from the PyTorch website</a>.</p></content><author><name>Thomas Viehmann, MathInf GmbH</name></author><summary type="html"></summary></entry><entry><title type="html">TinyML - How TVM is Taming Ti [...]
<p>The proliferation of low-cost, AI-powered consumer devices has led to widespread interest in “bare-metal” (low-power, often without an operating system) devices among ML researchers and practitioners. While it is already possible for experts to run <em>some</em> models on <em>some</em> bare-metal devices, optimizing models for diverse sets of devices is challenging, often requiring manually optimized device-specific libraries. And for those platforms wi [...]
@@ -1279,7 +1279,7 @@ Diagram from CMSIS-NN paper showing a 2x2 matrix multiplication microkernel</
<li><a href="https://homes.cs.washington.edu/~moreau/">Thierry Moreau</a>, for mentoring me during my time at OctoML.</li>
<li><a href="https://homes.cs.washington.edu/~vegaluis/">Luis Vega</a>, for teaching me the fundamentals of interacting with microcontrollers.</li>
<li><a href="https://www.linkedin.com/in/themadrasi/?originalSubdomain=uk">Ramana Radhakrishnan</a>, for supplying the Arm hardware used in our experiments and for providing guidance on its usage.</li>
-</ul></content><author><name>Logan Weber and Andrew Reusch, OctoML</name></author><summary type="html"></summary></entry><entry><title type="html">Bring Your Own Datatypes: Enabling Custom Datatype Exploration in TVM</title><link href="/2020/05/20/bring-your-own-datatypes" rel="alternate" type="text/html" title="Bring Your Own Datatypes: Enabling Custom Datatype Exploration in TVM" /><published>2020-05-20T00:00:00-07:00</published><updated>2020-05-20T00:00:00-07:00</updated><id>/20 [...]
+</ul></content><author><name>Logan Weber and Andrew Reusch, OctoML</name></author><summary type="html"></summary></entry><entry><title type="html">Bring Your Own Datatypes: Enabling Custom Datatype Exploration in TVM</title><link href="/2020/05/20/bring-your-own-datatypes" rel="alternate" type="text/html" title="Bring Your Own Datatypes: Enabling Custom Datatype Exploration in TVM" /><published>2020-05-20T00:00:00-04:00</published><updated>2020-05-20T00:00:00-04:00</updated><id>/20 [...]
<h2 id="introduction">Introduction</h2>
@@ -1556,7 +1556,7 @@ The Bring Your Own Datatypes framework
<p><a href="https://cloud.google.com/tpu/docs/bfloat16" target="_blank">Using bfloat16 with TensorFlow models</a> <a href="#fnref:tensorflowbfloat" class="reversefootnote" role="doc-backlink">&#8617;</a></p>
</li>
</ol>
-</div></content><author><name>Gus Smith</name></author><summary type="html">In this post, we describe the Bring Your Own Datatypes framework, which enables the use of custom datatypes within TVM.</summary></entry><entry><title type="html">Compiling Machine Learning to WASM and WebGPU with Apache TVM</title><link href="/2020/05/14/compiling-machine-learning-to-webassembly-and-webgpu" rel="alternate" type="text/html" title="Compiling Machine Learning to WASM and WebGPU with Apache TV [...]
+</div></content><author><name>Gus Smith</name></author><summary type="html">In this post, we describe the Bring Your Own Datatypes framework, which enables the use of custom datatypes within TVM.</summary></entry><entry><title type="html">Compiling Machine Learning to WASM and WebGPU with Apache TVM</title><link href="/2020/05/14/compiling-machine-learning-to-webassembly-and-webgpu" rel="alternate" type="text/html" title="Compiling Machine Learning to WASM and WebGPU with Apache TV [...]
<p>We introduced support for WASM and WebGPU to the Apache TVM deep learning compiler. Our experiments shows that TVM’s WebGPU backend can get <strong>close to native</strong> <strong>GPU performance</strong> when deploying models to the web.</p>
@@ -1634,7 +1634,7 @@ The Bring Your Own Datatypes framework
<h2 id="acknowledgement">Acknowledgement</h2>
-<p>We would like to thank the emscripten project for providing the WASM compilation infrastructures as well as the JS library support on the web. We would also like to thank the WebGPU community for various helpful discussions. Thanks to Fletcher Haynes for valuable feedbacks to the post.</p></content><author><name>Tianqi Chen and Jared Roesch, OctoML</name></author><summary type="html">TLDR</summary></entry><entry><title type="html">Integrating TVM into PyTorch</title><link [...]
+<p>We would like to thank the emscripten project for providing the WASM compilation infrastructures as well as the JS library support on the web. We would also like to thank the WebGPU community for various helpful discussions. Thanks to Fletcher Haynes for valuable feedbacks to the post.</p></content><author><name>Tianqi Chen and Jared Roesch, OctoML</name></author><summary type="html">TLDR</summary></entry><entry><title type="html">Integrating TVM into PyTorch</title><link [...]
it has become clear that PyTorch stands to benefit from directly leveraging the compiler stack.
A major tenet of PyTorch is providing seamless and robust integrations that don’t get in the user’s way.
To that end, PyTorch now has an official TVM-based backend, <a href="https://github.com/pytorch/tvm">torch_tvm</a>.</p>
@@ -1726,7 +1726,7 @@ def mul(a, b, c):
# via script
relay_graph = torch_tvm.to_relay(mul, inputs)
-</code></pre></div></div></content><author><name>Bram Wasti</name></author><summary type="html">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. A major tenet of PyTorch is providing seamless and robust integrations that don’t get in the user’s way. To that end, PyTorch now has an official TVM-based backend, torch_tvm.</summary [...]
+</code></pre></div></div></content><author><name>Bram Wasti</name></author><summary type="html">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. A major tenet of PyTorch is providing seamless and robust integrations that don’t get in the user’s way. To that end, PyTorch now has an official TVM-based backend, torch_tvm.</summary [...]
On real-time scenarios such as inference on autonomous vehicles, the inference speed of the model is critical.
Network quantization is an effective approach to accelerating deep learning models.
In quantized models, both data and model parameters are represented with low precision data types such as <code class="language-plaintext highlighter-rouge">int8</code> and <code class="language-plaintext highlighter-rouge">float16</code>.
@@ -1861,7 +1861,7 @@ We show that automatic optimization in TVM makes it easy and flexible to support
</ul>
<h1 id="bio--acknowledgement">Bio &amp; Acknowledgement</h1>
-<p><a href="https://wuwei.io/">Wuwei Lin</a> is an undergraduate student at SJTU. He is currently an intern at TuSimple. The author has many thanks to <a href="https://homes.cs.washington.edu/~tqchen/">Tianqi Chen</a> and <a href="https://homes.cs.washington.edu/~eqy/">Eddie Yan</a> for their reviews.</p></content><author><name>Wuwei Lin</name></author><summary type="html">Deep learning has been successfully ap [...]
+<p><a href="https://wuwei.io/">Wuwei Lin</a> is an undergraduate student at SJTU. He is currently an intern at TuSimple. The author has many thanks to <a href="https://homes.cs.washington.edu/~tqchen/">Tianqi Chen</a> and <a href="https://homes.cs.washington.edu/~eqy/">Eddie Yan</a> for their reviews.</p></content><author><name>Wuwei Lin</name></author><summary type="html">Deep learning has been successfully ap [...]
<p>TVM is an open source deep learning compiler stack that closes the gap between the productivity-focused deep learning frameworks, and the performance- or efficiency-oriented hardware backends. Today, we are glad to announce that the TVM community has decided to move on to Apache incubator, and becomes an Apache(incubating) project.</p>
@@ -1875,7 +1875,7 @@ We show that automatic optimization in TVM makes it easy and flexible to support
<p>We would like to take this chance to thank the Allen School for supporting the SAMPL team that gave birth to the TVM project. We would also like to thank the Halide project which provided the basis for TVM’s loop-level IR and initial code generation. We would like to thank our Apache incubator mentors for introducing the project to Apache and providing useful guidance. Finally, we would like to thank the TVM community and all of the organizations, as listed above, that supported [...]
-<p>See also the <a href="https://news.cs.washington.edu/2019/03/18/allen-schools-tvm-deep-learning-compiler-framework-transitions-to-apache/">Allen School news about the transition here</a>, <a href="https://sampl.cs.washington.edu/tvmconf/#about-tvmconf">TVM conference program slides and recordings</a>, and <a href="https://tvm.apache.org/docs//contribute/community.html">our community guideline here</a>. Follow us o [...]
+<p>See also the <a href="https://news.cs.washington.edu/2019/03/18/allen-schools-tvm-deep-learning-compiler-framework-transitions-to-apache/">Allen School news about the transition here</a>, <a href="https://sampl.cs.washington.edu/tvmconf/#about-tvmconf">TVM conference program slides and recordings</a>, and <a href="https://tvm.apache.org/docs//contribute/community.html">our community guideline here</a>. Follow us o [...]
<p>TVM is an open deep learning compiler stack to compile various deep learning models from different
frameworks to CPU, GPU or specialized accelerators. TVM supports model compilation from a wide range
@@ -2036,7 +2036,7 @@ closure as TVM packed function and invoke the same across programming language b
<li>[5] <a href="https://blog.learngoprogramming.com/golang-variadic-funcs-how-to-patterns-369408f19085">Go Variadic Functions</a></li>
<li>[6] <a href="https://github.com/jdeng/gomxnet">CFFI Ref</a></li>
<li>[7] <a href="https://golang.org/pkg/runtime/#SetFinalizer">Go Finalizers</a></li>
-</ul></content><author><name>Siva</name></author><summary type="html">Introduction</summary></entry><entry><title type="html">Automating Generation of Low Precision Deep Learning Operators</title><link href="/2018/12/18/lowprecision-conv" rel="alternate" type="text/html" title="Automating Generation of Low Precision Deep Learning Operators" /><published>2018-12-18T00:00:00-08:00</published><updated>2018-12-18T00:00:00-08:00</updated><id>/2018/12/18/lowprecision-conv</id><content ty [...]
+</ul></content><author><name>Siva</name></author><summary type="html">Introduction</summary></entry><entry><title type="html">Automating Generation of Low Precision Deep Learning Operators</title><link href="/2018/12/18/lowprecision-conv" rel="alternate" type="text/html" title="Automating Generation of Low Precision Deep Learning Operators" /><published>2018-12-18T00:00:00-05:00</published><updated>2018-12-18T00:00:00-05:00</updated><id>/2018/12/18/lowprecision-conv</id><content ty [...]
devices becomes challenging because of their limited compute and energy budgets. A recent trend
in deep learning is the use of extremely quantized models that operate on inputs and
weights of a few bits, with networks like XNOR-Net, DoReFa-Net, and HWGQ-Net making steady
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<link>https://tvm.apache.org</link>
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- <lastBuildDate>Sun, 27 Sep 2020 17:05:42 -0700</lastBuildDate>
- <pubDate>Sun, 27 Sep 2020 17:05:42 -0700</pubDate>
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+ <pubDate>Fri, 02 Oct 2020 14:07:08 -0400</pubDate>
<ttl>60</ttl>
@@ -486,7 +486,7 @@ Figure 4: After Graph Partitioning.
</description>
<link>https://tvm.apache.org/2020/07/15/how-to-bring-your-own-codegen-to-tvm</link>
<guid>https://tvm.apache.org/2020/07/15/how-to-bring-your-own-codegen-to-tvm</guid>
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</description>
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<guid>https://tvm.apache.org/2020/07/14/bert-pytorch-tvm</guid>
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</description>
<link>https://tvm.apache.org/2020/06/04/tinyml-how-tvm-is-taming-tiny</link>
<guid>https://tvm.apache.org/2020/06/04/tinyml-how-tvm-is-taming-tiny</guid>
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+ <pubDate>Thu, 04 Jun 2020 00:00:00 -0400</pubDate>
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</description>
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<guid>https://tvm.apache.org/2020/05/20/bring-your-own-datatypes</guid>
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<guid>https://tvm.apache.org/2020/05/14/compiling-machine-learning-to-webassembly-and-webgpu</guid>
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</description>
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<guid>https://tvm.apache.org/2019/05/30/pytorch-frontend</guid>
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</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 09:00:00 -0700</pubDate>
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</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 -0700</pubDate>
+ <pubDate>Mon, 18 Mar 2019 00:00:00 -0400</pubDate>
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@@ -2130,7 +2130,7 @@ 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 -0800</pubDate>
+ <pubDate>Sat, 19 Jan 2019 00:00:00 -0500</pubDate>
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@@ -2291,7 +2291,7 @@ 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 -0800</pubDate>
+ <pubDate>Tue, 18 Dec 2018 00:00:00 -0500</pubDate>
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@@ -2407,7 +2407,7 @@ 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 -0700</pubDate>
+ <pubDate>Tue, 09 Oct 2018 00:00:00 -0400</pubDate>
</item>
<item>
@@ -2801,7 +2801,7 @@ 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 -0700</pubDate>
+ <pubDate>Wed, 03 Oct 2018 00:00:00 -0400</pubDate>
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@@ -2940,7 +2940,7 @@ 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 -0700</pubDate>
+ <pubDate>Fri, 10 Aug 2018 00:00:00 -0400</pubDate>
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@@ -3082,7 +3082,7 @@ This kind of high-level visibility is essential to system designers who want to
</description>
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<guid>https://tvm.apache.org/2018/07/12/vta-release-announcement</guid>
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@@ -3348,7 +3348,7 @@ C = tvm.compute(
</description>
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<guid>https://tvm.apache.org/2018/03/23/nmt-transformer-optimize</guid>
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@@ -3464,7 +3464,7 @@ 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>
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@@ -4038,7 +4038,7 @@ advice and <a href="https://github.com/yzhliu">Yizhi Liu</a&g
</description>
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<guid>https://tvm.apache.org/2018/01/16/opt-mali-gpu</guid>
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<link>https://tvm.apache.org/2017/11/08/android-rpc-introduction</link>
<guid>https://tvm.apache.org/2017/11/08/android-rpc-introduction</guid>
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</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>
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</description>
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