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The following commit(s) were added to refs/heads/asf-site by this push:
new 6d8d4ea Build at Thu Jan 6 07:51:23 PM EST 2022
6d8d4ea is described below
commit 6d8d4ea8bf335ca643f05db2d78cbf1bf9c53804
Author: Wuwei Lin <[email protected]>
AuthorDate: Thu Jan 6 19:51:24 2022 -0500
Build at Thu Jan 6 07:51:23 PM EST 2022
---
2017/08/17/tvm-release-announcement.html | 2 +-
...s-with-TVM-A-Depthwise-Convolution-Example.html | 2 +-
2017/10/06/nnvm-compiler-announcement.html | 2 +-
...s-to-TVM-Stack-and-NNVM-Compiler-with-ROCm.html | 2 +-
2017/11/08/android-rpc-introduction.html | 2 +-
2018/01/16/opt-mali-gpu.html | 2 +-
2018/03/12/webgl.html | 2 +-
2018/03/23/nmt-transformer-optimize.html | 2 +-
2018/07/12/vta-release-announcement.html | 2 +-
2018/08/10/DLPack-Bridge.html | 2 +-
2018/10/03/auto-opt-all.html | 2 +-
2018/10/09/ml-in-tees.html | 2 +-
2018/12/18/lowprecision-conv.html | 2 +-
2019/01/19/Golang.html | 2 +-
2019/03/18/tvm-apache-announcement.html | 2 +-
2019/04/29/opt-cuda-quantized.html | 2 +-
2019/05/30/pytorch-frontend.html | 2 +-
...machine-learning-to-webassembly-and-webgpu.html | 2 +-
2020/06/04/tinyml-how-tvm-is-taming-tiny.html | 2 +-
2020/07/14/bert-pytorch-tvm.html | 2 +-
.../15/how-to-bring-your-own-codegen-to-tvm.html | 2 +-
2020/09/26/bring-your-own-datatypes.html | 2 +-
2021/03/03/intro-auto-scheduler.html | 2 +-
2021/12/15/tvm-unity.html | 2 +-
atom.xml | 42 ++++++++++-----------
download.html | 14 +++----
feed.xml | 20 +++++-----
rss.xml | 44 +++++++++++-----------
28 files changed, 84 insertions(+), 84 deletions(-)
diff --git a/2017/08/17/tvm-release-announcement.html
b/2017/08/17/tvm-release-announcement.html
index e170c13..8ac6f15 100644
--- a/2017/08/17/tvm-release-announcement.html
+++ b/2017/08/17/tvm-release-announcement.html
@@ -141,7 +141,7 @@
<div class="span14 w-100">
<h1>TVM: An End to End IR Stack for Deploying Deep Learning Workloads on
Hardware Platforms </h1>
<p class="post-meta">
- <time datetime="2017-08-17T12:00:00-07:00" 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 6577897..76db443 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
@@ -141,7 +141,7 @@
<div class="span14 w-100">
<h1>Optimize Deep Learning GPU Operators with TVM: A Depthwise
Convolution Example </h1>
<p class="post-meta">
- <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 1d4fa5f..a20703f 100644
--- a/2017/10/06/nnvm-compiler-announcement.html
+++ b/2017/10/06/nnvm-compiler-announcement.html
@@ -141,7 +141,7 @@
<div class="span14 w-100">
<h1>NNVM Compiler: Open Compiler for AI Frameworks </h1>
<p class="post-meta">
- <time datetime="2017-10-06T08:30:00-07:00" itemprop="datePublished">
+ <time datetime="2017-10-06T11:30:00-04:00" 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 359f4ff..c664cc6 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
@@ -141,7 +141,7 @@
<div class="span14 w-100">
<h1>Bringing AMDGPUs to TVM Stack and NNVM Compiler with ROCm </h1>
<p class="post-meta">
- <time datetime="2017-10-30T00:00:00-07:00" itemprop="datePublished">
+ <time datetime="2017-10-30T00:00:00-04:00" 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 1140d8c..2e80d5c 100644
--- a/2017/11/08/android-rpc-introduction.html
+++ b/2017/11/08/android-rpc-introduction.html
@@ -141,7 +141,7 @@
<div class="span14 w-100">
<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 2900e2a..910a998 100644
--- a/2018/01/16/opt-mali-gpu.html
+++ b/2018/01/16/opt-mali-gpu.html
@@ -141,7 +141,7 @@
<div class="span14 w-100">
<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
</time>
diff --git a/2018/03/12/webgl.html b/2018/03/12/webgl.html
index 3887d1d..a07ce08 100644
--- a/2018/03/12/webgl.html
+++ b/2018/03/12/webgl.html
@@ -141,7 +141,7 @@
<div class="span14 w-100">
<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 8c71ec1..1207cc9 100644
--- a/2018/03/23/nmt-transformer-optimize.html
+++ b/2018/03/23/nmt-transformer-optimize.html
@@ -141,7 +141,7 @@
<div class="span14 w-100">
<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
</time>
diff --git a/2018/07/12/vta-release-announcement.html
b/2018/07/12/vta-release-announcement.html
index de1d321..6e269d8 100644
--- a/2018/07/12/vta-release-announcement.html
+++ b/2018/07/12/vta-release-announcement.html
@@ -141,7 +141,7 @@
<div class="span14 w-100">
<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
</time>
diff --git a/2018/08/10/DLPack-Bridge.html b/2018/08/10/DLPack-Bridge.html
index 32f7c03..16ff4d4 100644
--- a/2018/08/10/DLPack-Bridge.html
+++ b/2018/08/10/DLPack-Bridge.html
@@ -141,7 +141,7 @@
<div class="span14 w-100">
<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">
+ <time datetime="2018-08-10T00:00:00-04:00" 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 49b8094..52c7392 100644
--- a/2018/10/03/auto-opt-all.html
+++ b/2018/10/03/auto-opt-all.html
@@ -141,7 +141,7 @@
<div class="span14 w-100">
<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">
+ <time datetime="2018-10-03T00:00:00-04:00" 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 54039dc..40d3e08 100644
--- a/2018/10/09/ml-in-tees.html
+++ b/2018/10/09/ml-in-tees.html
@@ -141,7 +141,7 @@
<div class="span14 w-100">
<h1>Efficient Privacy-Preserving ML Using TVM </h1>
<p class="post-meta">
- <time datetime="2018-10-09T00:00:00-07:00" itemprop="datePublished">
+ <time datetime="2018-10-09T00:00:00-04:00" itemprop="datePublished">
Oct 9, 2018
</time>
diff --git a/2018/12/18/lowprecision-conv.html
b/2018/12/18/lowprecision-conv.html
index ea08b7d..507cb57 100644
--- a/2018/12/18/lowprecision-conv.html
+++ b/2018/12/18/lowprecision-conv.html
@@ -141,7 +141,7 @@
<div class="span14 w-100">
<h1>Automating Generation of Low Precision Deep Learning Operators </h1>
<p class="post-meta">
- <time datetime="2018-12-18T00:00:00-08:00" itemprop="datePublished">
+ <time datetime="2018-12-18T00:00:00-05:00" itemprop="datePublished">
Dec 18, 2018
</time>
diff --git a/2019/01/19/Golang.html b/2019/01/19/Golang.html
index 583f5b5..a7bb455 100644
--- a/2019/01/19/Golang.html
+++ b/2019/01/19/Golang.html
@@ -141,7 +141,7 @@
<div class="span14 w-100">
<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
index ab1dd0a..35bd004 100644
--- a/2019/03/18/tvm-apache-announcement.html
+++ b/2019/03/18/tvm-apache-announcement.html
@@ -141,7 +141,7 @@
<div class="span14 w-100">
<h1>TVM Deep Learning Compiler Joins Apache Software Foundation </h1>
<p class="post-meta">
- <time datetime="2019-03-18T00:00:00-07:00" itemprop="datePublished">
+ <time datetime="2019-03-18T00:00:00-04:00" 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 ac324d1..fad10a6 100644
--- a/2019/04/29/opt-cuda-quantized.html
+++ b/2019/04/29/opt-cuda-quantized.html
@@ -141,7 +141,7 @@
<div class="span14 w-100">
<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">
+ <time datetime="2019-04-29T12:00:00-04:00" itemprop="datePublished">
Apr 29, 2019
</time>
diff --git a/2019/05/30/pytorch-frontend.html b/2019/05/30/pytorch-frontend.html
index a5914db..738f1a2 100644
--- a/2019/05/30/pytorch-frontend.html
+++ b/2019/05/30/pytorch-frontend.html
@@ -141,7 +141,7 @@
<div class="span14 w-100">
<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>
diff --git
a/2020/05/14/compiling-machine-learning-to-webassembly-and-webgpu.html
b/2020/05/14/compiling-machine-learning-to-webassembly-and-webgpu.html
index 3555788..d15468d 100644
--- a/2020/05/14/compiling-machine-learning-to-webassembly-and-webgpu.html
+++ b/2020/05/14/compiling-machine-learning-to-webassembly-and-webgpu.html
@@ -141,7 +141,7 @@
<div class="span14 w-100">
<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>
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 bc60762..275a693 100644
--- a/2020/06/04/tinyml-how-tvm-is-taming-tiny.html
+++ b/2020/06/04/tinyml-how-tvm-is-taming-tiny.html
@@ -141,7 +141,7 @@
<div class="span14 w-100">
<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 f10182e..874fd36 100644
--- a/2020/07/14/bert-pytorch-tvm.html
+++ b/2020/07/14/bert-pytorch-tvm.html
@@ -141,7 +141,7 @@
<div class="span14 w-100">
<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 cc76e94..a1d37d2 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
@@ -141,7 +141,7 @@
<div class="span14 w-100">
<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/2020/09/26/bring-your-own-datatypes.html
b/2020/09/26/bring-your-own-datatypes.html
index 2e65863..1c76068 100644
--- a/2020/09/26/bring-your-own-datatypes.html
+++ b/2020/09/26/bring-your-own-datatypes.html
@@ -141,7 +141,7 @@
<div class="span14 w-100">
<h1>Bring Your Own Datatypes: Enabling Custom Datatype Exploration in
TVM </h1>
<p class="post-meta">
- <time datetime="2020-09-26T00:00:00-07:00" itemprop="datePublished">
+ <time datetime="2020-09-26T00:00:00-04:00" itemprop="datePublished">
Sep 26, 2020
</time>
diff --git a/2021/03/03/intro-auto-scheduler.html
b/2021/03/03/intro-auto-scheduler.html
index 4de5e40..0b91eeb 100644
--- a/2021/03/03/intro-auto-scheduler.html
+++ b/2021/03/03/intro-auto-scheduler.html
@@ -141,7 +141,7 @@
<div class="span14 w-100">
<h1>Introducing TVM Auto-scheduler (a.k.a. Ansor) </h1>
<p class="post-meta">
- <time datetime="2021-03-03T00:00:00-08:00" itemprop="datePublished">
+ <time datetime="2021-03-03T00:00:00-05:00" itemprop="datePublished">
Mar 3, 2021
</time>
diff --git a/2021/12/15/tvm-unity.html b/2021/12/15/tvm-unity.html
index 7cca6f8..f43642e 100644
--- a/2021/12/15/tvm-unity.html
+++ b/2021/12/15/tvm-unity.html
@@ -143,7 +143,7 @@
<div class="span14 w-100">
<h1>Apache TVM Unity: a vision for the ML software & hardware ecosystem
in 2022 </h1>
<p class="post-meta">
- <time datetime="2021-12-15T00:00:00-08:00" itemprop="datePublished">
+ <time datetime="2021-12-15T00:00:00-05:00" itemprop="datePublished">
Dec 15, 2021
</time>
diff --git a/atom.xml b/atom.xml
index 9f02f1d..4088013 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>2021-12-15T21:18:08-08:00</updated>
+ <updated>2022-01-06T19:51:22-05:00</updated>
<id>https://tvm.apache.org</id>
<author>
<name></name>
@@ -15,7 +15,7 @@
<entry>
<title>Apache TVM Unity: a vision for the ML software & hardware
ecosystem in 2022</title>
<link href="https://tvm.apache.org/2021/12/15/tvm-unity"/>
- <updated>2021-12-15T00:00:00-08:00</updated>
+ <updated>2021-12-15T00:00:00-05:00</updated>
<id>https://tvm.apache.org/2021/12/15/tvm-unity</id>
<content type="html"><p>Apache TVM Unity is a roadmap for the TVM
ecosystem in 2022. We see a broader shift coming in the way that machine
learning system stacks optimize for flexibility and agility in the face of a
rapidly changing hardware landscape. TVM will evolve to break down the
boundaries that constrain the ways current ML systems adapt to rapid changes in
ML models and the accelerators that implement them.</p>
@@ -129,7 +129,7 @@ This example shows all of these capabilities:</p>
<entry>
<title>Introducing TVM Auto-scheduler (a.k.a. Ansor)</title>
<link href="https://tvm.apache.org/2021/03/03/intro-auto-scheduler"/>
- <updated>2021-03-03T00:00:00-08:00</updated>
+ <updated>2021-03-03T00:00:00-05:00</updated>
<id>https://tvm.apache.org/2021/03/03/intro-auto-scheduler</id>
<content type="html"><p>Optimizing the execution speed of deep neural
networks is extremely hard with the growing
model size, operator diversity, and hardware heterogeneity.
@@ -259,7 +259,7 @@ sparse operators, low-precision operators, and dynamic
shape better.</p>
<entry>
<title>Bring Your Own Datatypes: Enabling Custom Datatype Exploration in
TVM</title>
<link href="https://tvm.apache.org/2020/09/26/bring-your-own-datatypes"/>
- <updated>2020-09-26T00:00:00-07:00</updated>
+ <updated>2020-09-26T00:00:00-04:00</updated>
<id>https://tvm.apache.org/2020/09/26/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>
@@ -552,7 +552,7 @@ For more documentation about the Bring Your Own Datatypes
framework
<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 [...]
@@ -1031,7 +1031,7 @@ 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>,
@@ -1554,7 +1554,7 @@ 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>
@@ -1863,7 +1863,7 @@ Diagram from CMSIS-NN paper showing a 2x2 matrix
multiplication microkernel</
<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>
@@ -1950,7 +1950,7 @@ Diagram from CMSIS-NN paper showing a 2x2 matrix
multiplication microkernel</
<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.
@@ -2052,7 +2052,7 @@ 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.
@@ -2196,7 +2196,7 @@ 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>
@@ -2219,7 +2219,7 @@ 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>
@@ -2389,7 +2389,7 @@ 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
@@ -2550,7 +2550,7 @@ 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>
@@ -2666,7 +2666,7 @@ 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:
@@ -3060,7 +3060,7 @@ 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.
@@ -3199,7 +3199,7 @@ 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>
@@ -3341,7 +3341,7 @@ 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>
@@ -3607,7 +3607,7 @@ 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>
@@ -3723,7 +3723,7 @@ 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.
@@ -4297,7 +4297,7 @@ 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.
diff --git a/download.html b/download.html
index ed62d42..e74aaa3 100644
--- a/download.html
+++ b/download.html
@@ -158,9 +158,9 @@ Choose your flavor of download from the following links:</p>
<tbody>
<tr>
<td>0.8.0</td>
- <td><a
href="https://dist.apache.org/repos/dist/release/tvm/tvm-v0.8.0/apache-tvm-src-v0.8.0.tar.gz";>apache-tvm-src-v0.8.0.tar.gz</a></td>
- <td><a
href="https://dist.apache.org/repos/dist/release/tvm/tvm-v0.8.0/apache-tvm-src-v0.8.0.tar.gz.asc";>.asc</a></td>
- <td><a
href="https://dist.apache.org/repos/dist/release/tvm/tvm-v0.8.0/apache-tvm-src-v0.8.0.tar.gz.sha512";>.sha512</a></td>
+ <td><a
href="https://downloads.apache.org/tvm/tvm-v0.8.0/apache-tvm-src-v0.8.0.tar.gz";>apache-tvm-src-v0.8.0.tar.gz</a></td>
+ <td><a
href="https://downloads.apache.org/tvm/tvm-v0.8.0/apache-tvm-src-v0.8.0.tar.gz.asc";>.asc</a></td>
+ <td><a
href="https://downloads.apache.org/tvm/tvm-v0.8.0/apache-tvm-src-v0.8.0.tar.gz.sha512";>.sha512</a></td>
</tr>
</tbody>
</table>
@@ -169,7 +169,7 @@ Choose your flavor of download from the following links:</p>
<h1 id="verify-the-integrity-of-the-files">Verify the Integrity of the
Files</h1>
-<p>It is essential that you verify the integrity of the downloaded file using
the PGP signature (.asc file) or a hash (.md5 or .sha file). Please read <a
href="https://www.apache.org/info/verification.html";>Verifying Apache Software
Foundation Releases</a> for more information on why you should verify our
releases.</p>
+<p>It is essential that you verify the integrity of the downloaded file using
the PGP signature (.asc file) or a hash (.sha512 file). Please read <a
href="https://www.apache.org/info/verification.html";>Verifying Apache Software
Foundation Releases</a> for more information on why you should verify our
releases.</p>
<p>The PGP signature can be verified using PGP or GPG. First download the <a
href="https://downloads.apache.org/tvm/KEYS";>KEYS</a> as well as the .asc
signature file for the relevant distribution. Make sure you get these files
from the main distribution site, rather than from a mirror. Then verify the
signatures using one of the following alternatives:</p>
@@ -189,17 +189,17 @@ Choose your flavor of download from the following
links:</p>
<p>Hashes can be calculated using GPG:</p>
-<div class="language-bash highlighter-rouge"><div class="highlight"><pre
class="highlight"><code><span class="nv">$ </span>gpg <span
class="nt">--print-md</span> SHA1 downloaded_file
+<div class="language-bash highlighter-rouge"><div class="highlight"><pre
class="highlight"><code><span class="nv">$ </span>gpg <span
class="nt">--print-md</span> SHA512 downloaded_file
</code></pre></div></div>
-<p>The output should be compared with the contents of the SHA1 file. Similarly
for other hashes (SHA512 MD5 etc) which may be provided.</p>
+<p>The output should be compared with the contents of the SHA512 file.</p>
<p>Windows 7 and later systems should all now have certUtil:</p>
<div class="language-bash highlighter-rouge"><div class="highlight"><pre
class="highlight"><code><span class="nv">$ </span>certUtil <span
class="nt">-hashfile</span> pathToFileToCheck
</code></pre></div></div>
-<p>Unix-like systems (and macOS) will have a utility called <code
class="language-plaintext highlighter-rouge">md5</code>, <code
class="language-plaintext highlighter-rouge">md5sum</code> or <code
class="language-plaintext highlighter-rouge">shasum</code>.</p>
+<p>Unix-like systems (and macOS) will have a utility called <code
class="language-plaintext highlighter-rouge">shasum</code>.</p>
</div>
</div>
diff --git a/feed.xml b/feed.xml
index fb811a8..7d5e2a2 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>2021-12-15T21:18:08-08:00</updated><id>/feed.xml</id><title
type="html">TVM</title><author><name>{"name"=>nil}</name></author><entry><title
type="html">Apache TVM Unity: a vision for the ML software &am [...]
+<?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>2022-01-06T19:51:22-05:00</updated><id>/feed.xml</id><title
type="html">TVM</title><author><name>{"name"=>nil}</name></author><entry><title
type="html">Apache TVM Unity: a vision for the ML software &am [...]
<h2 id="boundaries-in-the-modern-ml-system-stack">Boundaries
in the Modern ML System Stack</h2>
@@ -103,7 +103,7 @@ This example shows all of these capabilities:</p>
<p>Beyond TVM alone, the same forces that are driving TVM Unity exist
across the theory and practice of modern ML. Rapid changes to models, emerging
alternative hardware, and aging abstraction boundaries all point toward the
need for an integrated approach. We expect TVM to lead the way into the next
great industry-wide shift in ML systems.</p>
-<p>For more details about our vision for TVM, check out <a
href="https://www.tvmcon.org">TVMCon 2021</a> for more talks
and discussion.</p></content><author><name>Adrian Sampson, Tianqi Chen,
Jared Roesch</name></author><summary type="html">Apache TVM Unity is a roadmap
for the TVM ecosystem in 2022. We see a broader shift coming in the way that
machine learning system stacks optimize for flexibility and agility in the face
of a rapidly changing hardware [...]
+<p>For more details about our vision for TVM, check out <a
href="https://www.tvmcon.org">TVMCon 2021</a> for more talks
and discussion.</p></content><author><name>Adrian Sampson, Tianqi Chen,
Jared Roesch</name></author><summary type="html">Apache TVM Unity is a roadmap
for the TVM ecosystem in 2022. We see a broader shift coming in the way that
machine learning system stacks optimize for flexibility and agility in the face
of a rapidly changing hardware [...]
model size, operator diversity, and hardware heterogeneity.
From a computational perspective, deep neural networks are just layers and
layers of tensor computations.
These tensor computations, such as matmul and conv2d, can be easily described
by mathematical expressions.
@@ -223,7 +223,7 @@ sparse operators, low-precision operators, and dynamic
shape better.</p>
<p>[1] Tutorials: <a
href="https://tvm.apache.org/docs/tutorials/index.html#autoscheduler-template-free-auto-scheduling">https://tvm.apache.org/docs/tutorials/index.html#autoscheduler-template-free-auto-scheduling</a><br
/>
[2] Benchmark repo: <a
href="https://github.com/tlc-pack/TLCBench">https://github.com/tlc-pack/TLCBench</a><br
/>
[3] OSDI Paper: <a
href="https://arxiv.org/abs/2006.06762">Ansor : Generating
High-Performance Tensor Programs for Deep Learning</a><br />
-[4] Results on Apple M1 chip: <a
href="https://medium.com/octoml/on-the-apple-m1-beating-apples-core-ml-4-with-30-model-performance-improvements-9d94af7d1b2d">https://medium.com/octoml/on-the-apple-m1-beating-apples-core-ml-4-with-30-model-performance-improvements-9d94af7d1b2d</a>.</p></content><author><name>Lianmin
Zheng, Chengfan Jia, Minmin Sun, Zhao Wu, Cody Hao Yu</name></author><summary
type="html">Optimizing the execution speed of deep neural networks i [...]
+[4] Results on Apple M1 chip: <a
href="https://medium.com/octoml/on-the-apple-m1-beating-apples-core-ml-4-with-30-model-performance-improvements-9d94af7d1b2d">https://medium.com/octoml/on-the-apple-m1-beating-apples-core-ml-4-with-30-model-performance-improvements-9d94af7d1b2d</a>.</p></content><author><name>Lianmin
Zheng, Chengfan Jia, Minmin Sun, Zhao Wu, Cody Hao Yu</name></author><summary
type="html">Optimizing the execution speed of deep neural networks i [...]
<h2 id="introduction">Introduction</h2>
@@ -507,7 +507,7 @@ For more documentation about the Bring Your Own Datatypes
framework
<p><a
href="https://posithub.org/docs/BeatingFloatingPoint.pdf"
target="_blank">Beating Floating Point at its Own Game: Posit
Arithmetic</a> <a href="#fnref:posit"
class="reversefootnote"
role="doc-backlink">&#8617;</a></p>
</li>
</ol>
-</div></content><author><name>Gus Smith, Andrew
Liu</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">How to Bring Your Own Codegen
to TVM</title><link href="/2020/07/15/how-to-bring-your-own-codegen-to-tvm"
rel="alternate" type="text/html" title="How to Bring Your Own Codegen to TVM"
/><published>2020-07-15T00:00:00-07:00</published>< [...]
+</div></content><author><name>Gus Smith, Andrew
Liu</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">How to Bring Your Own Codegen
to TVM</title><link href="/2020/07/15/how-to-bring-your-own-codegen-to-tvm"
rel="alternate" type="text/html" title="How to Bring Your Own Codegen to TVM"
/><published>2020-07-15T00:00:00-04:00</published>< [...]
<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>
@@ -976,7 +976,7 @@ 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.
@@ -1489,7 +1489,7 @@ 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 [...]
@@ -1788,7 +1788,7 @@ 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">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 TVM"
/><published>2020-05-14T00:00:00-07:00</published><updated>2020-05-14T00:00:00-07:00</upd
[...]
+</ul></content><author><name>Logan Weber and Andrew Reusch,
OctoML</name></author><summary type="html"></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 TVM"
/><published>2020-05-14T00:00:00-04:00</published><updated>2020-05-14T00:00:00-04:00</upd
[...]
<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>
@@ -1866,7 +1866,7 @@ Diagram from CMSIS-NN paper showing a 2x2 matrix
multiplication microkernel</
<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>
@@ -1958,7 +1958,7 @@ 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>.
@@ -2093,7 +2093,7 @@ 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>
diff --git a/rss.xml b/rss.xml
index 89ea26b..77962ce 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, 15 Dec 2021 21:18:08 -0800</lastBuildDate>
- <pubDate>Wed, 15 Dec 2021 21:18:08 -0800</pubDate>
+ <lastBuildDate>Thu, 06 Jan 2022 19:51:22 -0500</lastBuildDate>
+ <pubDate>Thu, 06 Jan 2022 19:51:22 -0500</pubDate>
<ttl>60</ttl>
@@ -121,7 +121,7 @@ This example shows all of these capabilities:</p>
</description>
<link>https://tvm.apache.org/2021/12/15/tvm-unity</link>
<guid>https://tvm.apache.org/2021/12/15/tvm-unity</guid>
- <pubDate>Wed, 15 Dec 2021 00:00:00 -0800</pubDate>
+ <pubDate>Wed, 15 Dec 2021 00:00:00 -0500</pubDate>
</item>
<item>
@@ -251,7 +251,7 @@ sparse operators, low-precision operators, and dynamic
shape better.</p>
</description>
<link>https://tvm.apache.org/2021/03/03/intro-auto-scheduler</link>
<guid>https://tvm.apache.org/2021/03/03/intro-auto-scheduler</guid>
- <pubDate>Wed, 03 Mar 2021 00:00:00 -0800</pubDate>
+ <pubDate>Wed, 03 Mar 2021 00:00:00 -0500</pubDate>
</item>
<item>
@@ -544,7 +544,7 @@ For more documentation about the Bring Your Own Datatypes
framework
</description>
<link>https://tvm.apache.org/2020/09/26/bring-your-own-datatypes</link>
<guid>https://tvm.apache.org/2020/09/26/bring-your-own-datatypes</guid>
- <pubDate>Sat, 26 Sep 2020 00:00:00 -0700</pubDate>
+ <pubDate>Sat, 26 Sep 2020 00:00:00 -0400</pubDate>
</item>
<item>
@@ -1023,7 +1023,7 @@ 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>
- <pubDate>Wed, 15 Jul 2020 00:00:00 -0700</pubDate>
+ <pubDate>Wed, 15 Jul 2020 00:00:00 -0400</pubDate>
</item>
<item>
@@ -1546,7 +1546,7 @@ He is a PyTorch core developer and co-authored <a
href="https://www.mann
</description>
<link>https://tvm.apache.org/2020/07/14/bert-pytorch-tvm</link>
<guid>https://tvm.apache.org/2020/07/14/bert-pytorch-tvm</guid>
- <pubDate>Tue, 14 Jul 2020 00:00:00 -0700</pubDate>
+ <pubDate>Tue, 14 Jul 2020 00:00:00 -0400</pubDate>
</item>
<item>
@@ -1855,7 +1855,7 @@ Diagram from CMSIS-NN paper showing a 2x2 matrix
multiplication microkernel</
</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>
- <pubDate>Thu, 04 Jun 2020 00:00:00 -0700</pubDate>
+ <pubDate>Thu, 04 Jun 2020 00:00:00 -0400</pubDate>
</item>
<item>
@@ -1942,7 +1942,7 @@ Diagram from CMSIS-NN paper showing a 2x2 matrix
multiplication microkernel</
</description>
<link>https://tvm.apache.org/2020/05/14/compiling-machine-learning-to-webassembly-and-webgpu</link>
<guid>https://tvm.apache.org/2020/05/14/compiling-machine-learning-to-webassembly-and-webgpu</guid>
- <pubDate>Thu, 14 May 2020 00:00:00 -0700</pubDate>
+ <pubDate>Thu, 14 May 2020 00:00:00 -0400</pubDate>
</item>
<item>
@@ -2044,7 +2044,7 @@ 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 -0700</pubDate>
+ <pubDate>Thu, 30 May 2019 00:00:00 -0400</pubDate>
</item>
<item>
@@ -2188,7 +2188,7 @@ 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 09:00:00 -0700</pubDate>
+ <pubDate>Mon, 29 Apr 2019 12:00:00 -0400</pubDate>
</item>
<item>
@@ -2211,7 +2211,7 @@ 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 -0700</pubDate>
+ <pubDate>Mon, 18 Mar 2019 00:00:00 -0400</pubDate>
</item>
<item>
@@ -2381,7 +2381,7 @@ 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>
</item>
<item>
@@ -2542,7 +2542,7 @@ 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>
</item>
<item>
@@ -2658,7 +2658,7 @@ 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>
@@ -3052,7 +3052,7 @@ 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>
</item>
<item>
@@ -3191,7 +3191,7 @@ 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>
</item>
<item>
@@ -3333,7 +3333,7 @@ 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 -0700</pubDate>
+ <pubDate>Thu, 12 Jul 2018 00:00:00 -0400</pubDate>
</item>
<item>
@@ -3599,7 +3599,7 @@ 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 -0700</pubDate>
+ <pubDate>Fri, 23 Mar 2018 00:00:00 -0400</pubDate>
</item>
<item>
@@ -3715,7 +3715,7 @@ 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 -0700</pubDate>
+ <pubDate>Mon, 12 Mar 2018 00:00:00 -0400</pubDate>
</item>
<item>
@@ -4289,7 +4289,7 @@ 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 -0800</pubDate>
+ <pubDate>Tue, 16 Jan 2018 00:00:00 -0500</pubDate>
</item>
<item>
@@ -4517,7 +4517,7 @@ 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 -0800</pubDate>
+ <pubDate>Wed, 08 Nov 2017 00:00:00 -0500</pubDate>
</item>
