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The following commit(s) were added to refs/heads/main by this push:
new 17999e10e9 Remove Legacy Components (#45)
17999e10e9 is described below
commit 17999e10e969e2e7fb0dfd732ee1ae7cb0eda852
Author: Siyuan Feng <[email protected]>
AuthorDate: Sat Sep 7 19:35:38 2024 +0800
Remove Legacy Components (#45)
Remove:
- VTA page
- TVM Conference card in community page
- Roadmap card in community page
Update copyright year from 2023 to 2024
---
_data/community.yml | 8 --------
_data/menus.yml | 2 --
_layouts/default.html | 4 ++--
vta.md | 36 ------------------------------------
4 files changed, 2 insertions(+), 48 deletions(-)
diff --git a/_data/community.yml b/_data/community.yml
index 762f59fe6f..831d042e5b 100644
--- a/_data/community.yml
+++ b/_data/community.yml
@@ -7,18 +7,10 @@
des: We use discuss forum for general discussions and usage trouble
shooting. We welcome all topics related to the TVM stack.
buttonname: Join The Discussion
link: https://discuss.tvm.apache.org/
-- cardname: TVM Conference
- des: We hold a yearly conference on the state of the art for TVM and would
love for you to join us. You can also view videos from our past conferences
here.
- buttonname: Learn About the Conference
- link: https://tvmcon.org/
- cardname: Github Issues
des: We use our Github issue tracker for developer RFCs and roadmap
discussion.
buttonname: Github Issue Tracker
link: https://github.com/apache/tvm/issues/
-- cardname: Roadmap
- des: We are releasing our public roadmaps on github. Please reach out are
interested working in aspects that are not on the roadmap.
- buttonname: See The Public Roadmap
- link: https://github.com/apache/tvm/projects/1
- cardname: Contributing
des: As a community project, we welcome contributions! The package is
developed and used by the community.
buttonname: Contribute
diff --git a/_data/menus.yml b/_data/menus.yml
index df0be29cdb..2f70d9517d 100644
--- a/_data/menus.yml
+++ b/_data/menus.yml
@@ -2,8 +2,6 @@
link: /community
- name: Download
link: /download
-- name: VTA
- link: /vta
- name: Blog
link: /blog
- name: Docs
diff --git a/_layouts/default.html b/_layouts/default.html
index c804cc9cd6..4b82709ddb 100644
--- a/_layouts/default.html
+++ b/_layouts/default.html
@@ -38,7 +38,7 @@
<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">© 2023 Apache
Software Foundation | All right reserved</h5>
+ <h5 id="apache-software-foundation--all-right-reserved">© 2024 Apache
Software Foundation | All right reserved</h5>
</li>
</ul>
@@ -46,7 +46,7 @@
<ul class="container">
<li class="footernote">
- Copyright © 2023 The Apache Software Foundation. Apache TVM, Apache, the
Apache feather, and the Apache TVM project logo are either trademarks or
registered trademarks of the Apache Software Foundation.</li>
+ Copyright © 2024 The Apache Software Foundation. Apache TVM, Apache, the
Apache feather, and the Apache TVM project logo are either trademarks or
registered trademarks of the Apache Software Foundation.</li>
</ul>
</section>
diff --git a/vta.md b/vta.md
deleted file mode 100644
index 37aa9e5035..0000000000
--- a/vta.md
+++ /dev/null
@@ -1,36 +0,0 @@
----
-layout: page
-title: "VTA"
-order : 13
-group : navigation
-description: "VTA"
----
-{% include JB/setup %}
-
-# About VTA
-
-The Versatile Tensor Accelerator (VTA) is an extension of the
Apache(incubating) TVM framework designed to advance deep learning and hardware
innovation.
-VTA is a programmable accelerator that exposes a RISC-like programming
abstraction to describe compute and memory operations at the tensor level. We
designed VTA to expose the most salient and common characteristics of
mainstream deep learning accelerators, such as tensor operations, DMA
load/stores, and explicit compute/memory arbitration.
-
-VTA is more than a standalone accelerator design: it’s an end-to-end solution
that includes drivers, a JIT runtime, and an optimizing compiler stack based on
TVM.
-The current release includes a behavioral hardware simulator, as well as the
infrastructure to deploy VTA on low-cost FPGA hardware for fast prototyping.
-By extending the TVM stack with a customizable, and open source deep learning
hardware accelerator design, we are exposing a transparent end-to-end deep
learning stack from the high-level deep learning framework, down to the actual
hardware design and implementation.
-This forms a truly end-to-end, from software-to-hardware open source stack for
deep learning systems.
-
-{:center: style="text-align: center"}
-{:
width="50%"}
-{:center}
-
-The VTA and TVM stack together constitute a blueprint for end-to-end,
accelerator-centric deep learning system that can:
-
-- Provide an open deep learning system stack for hardware, compilers, and
systems researchers alike to incorporate optimizations and co-design techniques.
-- Lower the barrier of entry for machine learning practitioners to experiment
with novel network architectures, operators and data representations that
require specialized hardware support.
-
-
-VTA is a component of TVM which was a research project at the [SAMPL
group](https://sampl.cs.washington.edu/) of
-Paul G. Allen School of Computer Science & Engineering, University of
Washington.
-TVM is now an effort undergoing incubation at The Apache Software Foundation
(ASF),
-driven by an open source community involving multiple industry and academic
institutions
-under the Apache way.
-
-Read more about VTA in the [TVM blog
post](https://tvm.apache.org/2018/07/12/vta-release-announcement.html), or in
the [VTA techreport](https://arxiv.org/abs/1807.04188).
