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The following commit(s) were added to refs/heads/asf-site by this push:
new c19d9f5 Build at Fri Oct 9 20:03:48 EDT 2020
c19d9f5 is described below
commit c19d9f5366df7f4194713bed76c1e8ca43b8bd2d
Author: tqchen <[email protected]>
AuthorDate: Fri Oct 9 20:03:49 2020 -0400
Build at Fri Oct 9 20:03:48 EDT 2020
---
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 +-
atom.xml | 42 ++---
community.html | 174 +++++++++++++++++++++
feed.xml | 20 +--
rss.xml | 44 +++---
26 files changed, 249 insertions(+), 75 deletions(-)
diff --git a/2017/08/17/tvm-release-announcement.html
b/2017/08/17/tvm-release-announcement.html
index 5094adc..8d4d7fa 100644
--- a/2017/08/17/tvm-release-announcement.html
+++ b/2017/08/17/tvm-release-announcement.html
@@ -140,7 +140,7 @@
<div class="span14">
<h1>TVM: An End to End IR Stack for Deploying Deep Learning Workloads on
Hardware Platforms </h1>
<p class="post-meta">
- <time datetime="2017-08-17T12:00:00-07:00" 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 8d6fbeb..3a85ec6 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
@@ -140,7 +140,7 @@
<div class="span14">
<h1>Optimize Deep Learning GPU Operators with TVM: A Depthwise
Convolution Example </h1>
<p class="post-meta">
- <time datetime="2017-08-22T00:00:00-07:00" 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 bdfcade..89fa2da 100644
--- a/2017/10/06/nnvm-compiler-announcement.html
+++ b/2017/10/06/nnvm-compiler-announcement.html
@@ -140,7 +140,7 @@
<div class="span14">
<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 ec779e8..ef1c332 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
@@ -140,7 +140,7 @@
<div class="span14">
<h1>Bringing AMDGPUs to TVM Stack and NNVM Compiler with ROCm </h1>
<p class="post-meta">
- <time datetime="2017-10-30T00:00:00-07:00" 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 24e4cb7..7d9330f 100644
--- a/2017/11/08/android-rpc-introduction.html
+++ b/2017/11/08/android-rpc-introduction.html
@@ -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 b6f8aed..6554dc8 100644
--- a/2018/01/16/opt-mali-gpu.html
+++ b/2018/01/16/opt-mali-gpu.html
@@ -140,7 +140,7 @@
<div class="span14">
<h1>Optimizing Mobile Deep Learning on ARM GPU with TVM </h1>
<p class="post-meta">
- <time datetime="2018-01-16T00:00:00-08:00" 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 4870b1f..d39e78c 100644
--- a/2018/03/12/webgl.html
+++ b/2018/03/12/webgl.html
@@ -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 385adc1..ad70e44 100644
--- a/2018/03/23/nmt-transformer-optimize.html
+++ b/2018/03/23/nmt-transformer-optimize.html
@@ -140,7 +140,7 @@
<div class="span14">
<h1>Bringing TVM into TensorFlow for Optimizing Neural Machine
Translation on GPU </h1>
<p class="post-meta">
- <time datetime="2018-03-23T00:00:00-07:00" 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 8152a8e..4dec55e 100644
--- a/2018/07/12/vta-release-announcement.html
+++ b/2018/07/12/vta-release-announcement.html
@@ -140,7 +140,7 @@
<div class="span14">
<h1>VTA: An Open, Customizable Deep Learning Acceleration Stack </h1>
<p class="post-meta">
- <time datetime="2018-07-12T00:00:00-07:00" 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 e0ca096..fcd167f 100644
--- a/2018/08/10/DLPack-Bridge.html
+++ b/2018/08/10/DLPack-Bridge.html
@@ -140,7 +140,7 @@
<div class="span14">
<h1>Building a Cross-Framework Deep Learning Compiler via DLPack </h1>
<p class="post-meta">
- <time datetime="2018-08-10T00:00:00-07:00" 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 3d54540..af4207a 100644
--- a/2018/10/03/auto-opt-all.html
+++ b/2018/10/03/auto-opt-all.html
@@ -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">
+ <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 468516e..89f9234 100644
--- a/2018/10/09/ml-in-tees.html
+++ b/2018/10/09/ml-in-tees.html
@@ -140,7 +140,7 @@
<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">
+ <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 c94cade..d0c64c7 100644
--- a/2018/12/18/lowprecision-conv.html
+++ b/2018/12/18/lowprecision-conv.html
@@ -140,7 +140,7 @@
<div class="span14">
<h1>Automating Generation of Low Precision Deep Learning Operators </h1>
<p class="post-meta">
- <time datetime="2018-12-18T00:00:00-08:00" 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 c229ce8..edde55f 100644
--- a/2019/01/19/Golang.html
+++ b/2019/01/19/Golang.html
@@ -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
index 103903f..ea5144d 100644
--- a/2019/03/18/tvm-apache-announcement.html
+++ b/2019/03/18/tvm-apache-announcement.html
@@ -140,7 +140,7 @@
<div class="span14">
<h1>TVM Deep Learning Compiler Joins Apache Software Foundation </h1>
<p class="post-meta">
- <time datetime="2019-03-18T00:00:00-07:00" 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 9092c6f..58e728d 100644
--- a/2019/04/29/opt-cuda-quantized.html
+++ b/2019/04/29/opt-cuda-quantized.html
@@ -140,7 +140,7 @@
<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">
+ <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 88e4518..3580589 100644
--- a/2019/05/30/pytorch-frontend.html
+++ b/2019/05/30/pytorch-frontend.html
@@ -140,7 +140,7 @@
<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>
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 3fcfd89..2080c1e 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
@@ -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>
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 c313e89..7e0769f 100644
--- a/2020/06/04/tinyml-how-tvm-is-taming-tiny.html
+++ b/2020/06/04/tinyml-how-tvm-is-taming-tiny.html
@@ -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 31e7a6b..46c2117 100644
--- a/2020/07/14/bert-pytorch-tvm.html
+++ b/2020/07/14/bert-pytorch-tvm.html
@@ -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 4fca97a..adbb6ba 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
@@ -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/2020/09/26/bring-your-own-datatypes.html
b/2020/09/26/bring-your-own-datatypes.html
index 8476e23..f96c77e 100644
--- a/2020/09/26/bring-your-own-datatypes.html
+++ b/2020/09/26/bring-your-own-datatypes.html
@@ -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="2020-09-26T00:00:00-07:00" itemprop="datePublished">
+ <time datetime="2020-09-26T00:00:00-04:00" itemprop="datePublished">
Sep 26, 2020
</time>
diff --git a/atom.xml b/atom.xml
index 31b3d42..de47c9b 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>2020-10-09T17:01:33-07:00</updated>
+ <updated>2020-10-09T20:03:45-04:00</updated>
<id>https://tvm.apache.org</id>
<author>
<name></name>
@@ -15,7 +15,7 @@
<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>
@@ -308,7 +308,7 @@ 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 [...]
@@ -787,7 +787,7 @@ 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>,
@@ -1310,7 +1310,7 @@ 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>
@@ -1619,7 +1619,7 @@ 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>
@@ -1706,7 +1706,7 @@ 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.
@@ -1808,7 +1808,7 @@ 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.
@@ -1952,7 +1952,7 @@ 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>
@@ -1975,7 +1975,7 @@ 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>
@@ -2145,7 +2145,7 @@ 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
@@ -2306,7 +2306,7 @@ 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>
@@ -2422,7 +2422,7 @@ 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:
@@ -2816,7 +2816,7 @@ 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.
@@ -2955,7 +2955,7 @@ 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>
@@ -3097,7 +3097,7 @@ 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>
@@ -3363,7 +3363,7 @@ 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>
@@ -3479,7 +3479,7 @@ 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.
@@ -4053,7 +4053,7 @@ 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.
@@ -4281,7 +4281,7 @@ 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>
@@ -4507,7 +4507,7 @@ 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/community.html b/community.html
index 88036f1..282ab42 100644
--- a/community.html
+++ b/community.html
@@ -1,3 +1,137 @@
+<html lang="en">
+<head>
+ <meta charset="UTF-8">
+ <meta name="viewport" content="width=device-width, initial-scale=1.0">
+ <title>Community</title>
+ <link rel="shortcut icon" href="/assets/images/favicon.ico">
+ <link rel="stylesheet"
href="https://stackpath.bootstrapcdn.com/bootstrap/4.1.3/css/bootstrap.min.css";
integrity="sha384-MCw98/SFnGE8fJT3GXwEOngsV7Zt27NXFoaoApmYm81iuXoPkFOJwJ8ERdknLPMO"
crossorigin="anonymous">
+ <link rel="stylesheet" href="/css/slick.css">
+ <link rel="stylesheet" href="/css/slick-theme.css">
+ <link rel="stylesheet" href="/css/custom.css">
+</head>
+<body>
+
+ <div class="bannerPage">
+ <header class="header">
+ <div class="container">
+ <div class="headerInner d-flex justify-content-between
align-items-center">
+ <div class="headerLogo">
+ <a href="/"><img src="/assets/images/logo.svg" alt="Logo"></a>
+ </div>
+ <div id="headMenu" class="headerNav">
+ <button type="button" id="closeHeadMenu" class="navCloseBtn"><img
src="/assets/images/close-icon.svg"
+ alt="Close"></button>
+ <ul class="nav">
+
+ <li class="nav-item">
+ <a class="nav-link" href="/community">Community</a>
+ </li>
+
+ <li class="nav-item">
+ <a class="nav-link" href="/download">Download</a>
+ </li>
+
+ <li class="nav-item">
+ <a class="nav-link" href="/vta">VTA</a>
+ </li>
+
+ <li class="nav-item">
+ <a class="nav-link" href="/blog">Blog</a>
+ </li>
+
+ <li class="nav-item">
+ <a class="nav-link" href="https://tvm.apache.org/docs/";>Docs</a>
+ </li>
+
+ <li class="nav-item">
+ <a class="nav-link" href="https://tvmconf.org/";>Conference</a>
+ </li>
+
+ <li class="nav-item">
+ <a class="nav-link"
href="https://github.com/apache/incubator-tvm/";>Github</a>
+ </li>
+
+</ul>
+ <div class="responsiveasfdropdown">
+ <button type="button" class="btn-link">
+ ASF
+ </button>
+ <ul>
+
+ <li>
+ <a href="https://www.apache.org/";>Apache Homepage</a>
+ </li>
+
+ <li>
+ <a href="https://www.apache.org/licenses/";>License</a>
+ </li>
+
+ <li>
+ <a
href="https://www.apache.org/foundation/sponsorship.html";>Sponsorship</a>
+ </li>
+
+ <li>
+ <a href="https://www.apache.org/security/";>Security</a>
+ </li>
+
+ <li>
+ <a href="https://www.apache.org/foundation/thanks.html";>Thanks</a>
+ </li>
+
+ <li>
+ <a href="https://www.apache.org/events/current-event";>Events</a>
+ </li>
+
+</ul>
+ </div>
+ </div>
+ <div class="responsiveMenuIcon">
+ <button type="button" id="menuBtn" class="btn-menu"><img
src="/assets/images/menu-icon.svg"
+ alt="Menu Icon" /></button>
+ </div>
+ <div class="asfDropdown">
+ <div class="dropdown">
+ <button type="button" class="btn-link dropdown-toggle"
data-toggle="dropdown" aria-haspopup="true"
+ aria-expanded="false">
+ ASF
+ </button>
+ <div class="dropdown-menu dropdown-menu-right">
+ <ul>
+
+ <li>
+ <a href="https://www.apache.org/";>Apache Homepage</a>
+ </li>
+
+ <li>
+ <a href="https://www.apache.org/licenses/";>License</a>
+ </li>
+
+ <li>
+ <a
href="https://www.apache.org/foundation/sponsorship.html";>Sponsorship</a>
+ </li>
+
+ <li>
+ <a href="https://www.apache.org/security/";>Security</a>
+ </li>
+
+ <li>
+ <a href="https://www.apache.org/foundation/thanks.html";>Thanks</a>
+ </li>
+
+ <li>
+ <a href="https://www.apache.org/events/current-event";>Events</a>
+ </li>
+
+</ul>
+ </div>
+ </div>
+ </div>
+ </div>
+ </div>
+ </header>
+
+</div>
+
<section class="communitySec">
<div class="container">
@@ -164,3 +298,43 @@ This is a community maintained list of organizations using
and contributing to t
</div>
</section>
+
+
+
+
+
+
+
+ <script src="https://code.jquery.com/jquery-2.2.0.min.js";
type="text/javascript"></script>
+ <script
src="https://cdnjs.cloudflare.com/ajax/libs/popper.js/1.14.3/umd/popper.min.js";
integrity="sha384-ZMP7rVo3mIykV+2+9J3UJ46jBk0WLaUAdn689aCwoqbBJiSnjAK/l8WvCWPIPm49"
crossorigin="anonymous"></script>
+ <script
src="https://stackpath.bootstrapcdn.com/bootstrap/4.1.3/js/bootstrap.min.js";
integrity="sha384-ChfqqxuZUCnJSK3+MXmPNIyE6ZbWh2IMqE241rYiqJxyMiZ6OW/JmZQ5stwEULTy"
crossorigin="anonymous"></script>
+ <!-- <script src="./assets/js/slick.js"></script> -->
+ <script src="/assets/js/custome.js"></script>
+ <script async
src="https://www.googletagmanager.com/gtag/js?id=UA-75982049-2";></script>
+ <script>
+ window.dataLayer = window.dataLayer || [];
+ function gtag(){dataLayer.push(arguments);}
+ gtag('js', new Date());
+ gtag('config', 'UA-75982049-2');
+ </script>
+</body>
+<section class="footerSec">
+ <div class="footerHeader">
+ <ul class="container d-flex align-md-items-center justify-content-between
flex-column flex-md-row">
+ <li class="logo">
+
+ <p><a href="/"><img src="/assets/images/logo.svg" alt="logo"
title="logo" /></a></p>
+ </li>
+ <li class="copywrite d-flex align-items-center">
+ <h5 id="apache-software-foundation--all-right-reserved">© 2020 Apache
Software Foundation | All right reserved</h5>
+ </li>
+ </ul>
+
+ </div>
+
+ <ul class="container">
+ <li class="footernote">Apache TVM is an effort undergoing incubation at
The Apache Software Foundation (ASF), sponsored by the Apache Incubator.
Incubation is required of all newly accepted projects until a further review
indicates that the infrastructure, communications, and decision making process
have stabilized in a manner consistent with other successful ASF projects.
While incubation status is not necessarily a reflection of the completeness or
stability of the code, it does in [...]
+ </ul>
+
+</section>
+</html>
diff --git a/feed.xml b/feed.xml
index 4bf3058..061174d 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>2020-10-09T17:01:33-07:00</updated><id>/feed.xml</id><title
type="html">TVM</title><author><name>{"name"=>nil}</name></author><entry><title
type="html">Bring Your Own Datatypes: Enabling Custom Datatype [...]
+<?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>2020-10-09T20:03:45-04:00</updated><id>/feed.xml</id><title
type="html">TVM</title><author><name>{"name"=>nil}</name></author><entry><title
type="html">Bring Your Own Datatypes: Enabling Custom Datatype [...]
<h2 id="introduction">Introduction</h2>
@@ -282,7 +282,7 @@ 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>
@@ -751,7 +751,7 @@ 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.
@@ -1264,7 +1264,7 @@ 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 [...]
@@ -1563,7 +1563,7 @@ 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>
@@ -1641,7 +1641,7 @@ 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>
@@ -1733,7 +1733,7 @@ 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>.
@@ -1868,7 +1868,7 @@ 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>
@@ -1882,7 +1882,7 @@ 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
@@ -2043,7 +2043,7 @@ 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
diff --git a/rss.xml b/rss.xml
index d4b5cb3..b9d074f 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>Fri, 09 Oct 2020 17:01:33 -0700</lastBuildDate>
- <pubDate>Fri, 09 Oct 2020 17:01:33 -0700</pubDate>
+ <lastBuildDate>Fri, 09 Oct 2020 20:03:45 -0400</lastBuildDate>
+ <pubDate>Fri, 09 Oct 2020 20:03:45 -0400</pubDate>
<ttl>60</ttl>
@@ -300,7 +300,7 @@ 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>
@@ -779,7 +779,7 @@ 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>
@@ -1302,7 +1302,7 @@ 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>
@@ -1611,7 +1611,7 @@ 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>
@@ -1698,7 +1698,7 @@ 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>
@@ -1800,7 +1800,7 @@ 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>
@@ -1944,7 +1944,7 @@ 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>
@@ -1967,7 +1967,7 @@ 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>
@@ -2137,7 +2137,7 @@ 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>
@@ -2298,7 +2298,7 @@ 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>
@@ -2414,7 +2414,7 @@ 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>
@@ -2808,7 +2808,7 @@ 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>
@@ -2947,7 +2947,7 @@ 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>
@@ -3089,7 +3089,7 @@ 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>
@@ -3355,7 +3355,7 @@ 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>
@@ -3471,7 +3471,7 @@ 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>
@@ -4045,7 +4045,7 @@ 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>
@@ -4273,7 +4273,7 @@ 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>
<item>
@@ -4499,7 +4499,7 @@ BB0_6:
</description>
<link>https://tvm.apache.org/2017/10/30/Bringing-AMDGPUs-to-TVM-Stack-and-NNVM-Compiler-with-ROCm</link>
<guid>https://tvm.apache.org/2017/10/30/Bringing-AMDGPUs-to-TVM-Stack-and-NNVM-Compiler-with-ROCm</guid>
- <pubDate>Mon, 30 Oct 2017 00:00:00 -0700</pubDate>
+ <pubDate>Mon, 30 Oct 2017 00:00:00 -0400</pubDate>
</item>
<item>
@@ -4582,7 +4582,7 @@ We also learns from Halide when implementing the lowering
pipeline in TVM.</l
</description>
<link>https://tvm.apache.org/2017/10/06/nnvm-compiler-announcement</link>
<guid>https://tvm.apache.org/2017/10/06/nnvm-compiler-announcement</guid>
- <pubDate>Fri, 06 Oct 2017 08:30:00 -0700</pubDate>
+ <pubDate>Fri, 06 Oct 2017 11:30:00 -0400</pubDate>
</item>
