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DISCONTINUATION OF PROJECT

This project will no longer be maintained by Intel.
Intel has ceased development and contributions including, but not limited to, maintenance, bug fixes, new releases, or updates, to this project.
Intel no longer accepts patches to this project.
If you have an ongoing need to use this project, are interested in independently developing it, or would like to maintain patches for the open source software community, please create your own fork of this project.

CI

Android Neural Networks HAL with OpenVINO supporting hardware accelerators such as /

Intel® Math Kernel Library for Deep Neural Networks (Intel® MKL-DNN)

Introduction

The Android Neural Network Hardware Abstraction Layer(NN HAL) provides the hardware accelration for Android Neural Networks (NN) API. Intel NN-HAL takes the advantage of the Intel MKLD-DNN, enables high performance and low power implementation of Neural Networks API. Intel MKL-DNN https://github.com/intel/mkl-dnn & https://01.org/mkl-dnn Android NN API is on [Neural Networks API] (https://developer.android.com/ndk/guides/neuralnetworks/index.html). OpenVINO deep learning framework https://github.com/opencv/dldt & https://01.org/openvinotoolkit

Supported Operations

Following operations are currently supported by Android Neural Networks HAL for Intel MKL-DNN.

  • ANEURALNETWORKS_CONV_2D
  • ANEURALNETWORKS_ADD

Known issues

Support for Multiple Tensor inputs at runtime to model/network is ongoing

License

Android Neural Networks HAL is distributed under the Apache License, Version 2.0 You may obtain a copy of the License at: http://www.apache.org/licenses/LICENSE-2.0 Intel® Math Kernel Library for Deep Neural Networks (Intel® MKL-DNN) is an open source performance library for Deep Learning (DL) applications intended for acceleration of DL frameworks on Intel® architecture.

How to provide feedback

By default, please submit an issue using native github.com interface: https://github.com/intel/nn-hal/issues

How to contribute

Create a pull request on github.com with your patch. Make sure your change is cleanly building and passing ULTs.

A maintainer will contact you if there are questions or concerns.

Continuous Integration

Before committing any changes, make sure the coding style and testing configs are correct. If not, the CI will fail.

Coding Style

Run the following command to ensure that the proper coding style is being followed:

 find . -regex '.*\.\(cpp\|hpp\|cc\|cxx\|h\)' -exec clang-format -style=file -i {} \;

Build and Test

Update the BOARD value in build-test.sh as per your test requirement. If your BOARD is not supported, please contact the maintainer to get it added.

Currently, the CI builds the intel-nnhal package and runs the following tests:

  • Functional tests that include ml_cmdline and a subset of cts and vts tests.

About

No description, website, or topics provided.

Resources

Readme

Code of conduct

Code of conduct

Contributing

Contributing

Security policy

Security policy
Activity
Custom properties

Stars

46 stars

Watchers

4 watching

Forks

50 forks
Report repository

Releases

Packages

Used by

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

Skip to content

This repository was archived by the owner on Jul 18, 2024. It is now read-only.

BranchesTags
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DISCONTINUATION OF PROJECT
This project will no longer be maintained by Intel.

Intel has ceased development and contributions including, but not limited to, maintenance, bug fixes, new releases, or updates, to this project.

Intel no longer accepts patches to this project.

If you have an ongoing need to use this project, are interested in independently developing it, or would like to maintain patches for the open source software community, please create your own fork of this project.
CI
Android Neural Networks HAL with OpenVINO supporting hardware accelerators such as /
Intel® Math Kernel Library for Deep Neural Networks (Intel® MKL-DNN)
Introduction
The Android Neural Network Hardware Abstraction Layer(NN HAL) provides the hardware accelration
for Android Neural Networks (NN) API. Intel NN-HAL takes the advantage of the Intel MKLD-DNN,
enables high performance and low power implementation of Neural Networks API.
Intel MKL-DNN https://github.com/intel/mkl-dnn & https://01.org/mkl-dnn
Android NN API is on [Neural Networks API]
(https://developer.android.com/ndk/guides/neuralnetworks/index.html).
OpenVINO deep learning framework https://github.com/opencv/dldt & https://01.org/openvinotoolkit
Supported Operations
Following operations are currently supported by Android Neural Networks HAL for Intel MKL-DNN.
  • ANEURALNETWORKS_CONV_2D
  • ANEURALNETWORKS_ADD
Known issues
Support for Multiple Tensor inputs at runtime to model/network is ongoing
License
Android Neural Networks HAL is distributed under the Apache License, Version 2.0
You may obtain a copy of the License at: http://www.apache.org/licenses/LICENSE-2.0
Intel® Math Kernel Library for Deep Neural Networks (Intel® MKL-DNN) is an open source
performance library for Deep Learning (DL) applications intended for acceleration of DL
frameworks on Intel® architecture.
How to provide feedback
By default, please submit an issue using native github.com interface:
https://github.com/intel/nn-hal/issues
How to contribute
Create a pull request on github.com with your patch. Make sure your change is cleanly building
and passing ULTs.
A maintainer will contact you if there are questions or concerns.
Continuous Integration
Before committing any changes, make sure the coding style and testing configs are correct.
If not, the CI will fail.
Coding Style
Run the following command to ensure that the proper coding style is being followed:
 find . -regex '.*\.\(cpp\|hpp\|cc\|cxx\|h\)' -exec clang-format -style=file -i {} \;

Build and Test
Update the BOARD value in build-test.sh as per your test requirement.
If your BOARD is not supported, please contact the maintainer to get it added.
Currently, the CI builds the intel-nnhal package and runs the following tests:
  • Functional tests that include ml_cmdline and a subset of cts and vts tests.
About
No description, website, or topics provided.
Resources
Readme
Code of conduct
Code of conduct
Contributing
Contributing
Security policy
Security policy
Activity
Custom properties
Stars
46 stars
Watchers
4 watching
Forks
50 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content

This repository was archived by the owner on Jul 18, 2024. It is now read-only.

BranchesTags
Repository files navigation
DISCONTINUATION OF PROJECT
This project will no longer be maintained by Intel.

Intel has ceased development and contributions including, but not limited to, maintenance, bug fixes, new releases, or updates, to this project.

Intel no longer accepts patches to this project.

If you have an ongoing need to use this project, are interested in independently developing it, or would like to maintain patches for the open source software community, please create your own fork of this project.
CI
Android Neural Networks HAL with OpenVINO supporting hardware accelerators such as /
Intel® Math Kernel Library for Deep Neural Networks (Intel® MKL-DNN)
Introduction
The Android Neural Network Hardware Abstraction Layer(NN HAL) provides the hardware accelration
for Android Neural Networks (NN) API. Intel NN-HAL takes the advantage of the Intel MKLD-DNN,
enables high performance and low power implementation of Neural Networks API.
Intel MKL-DNN https://github.com/intel/mkl-dnn & https://01.org/mkl-dnn
Android NN API is on [Neural Networks API]
(https://developer.android.com/ndk/guides/neuralnetworks/index.html).
OpenVINO deep learning framework https://github.com/opencv/dldt & https://01.org/openvinotoolkit
Supported Operations
Following operations are currently supported by Android Neural Networks HAL for Intel MKL-DNN.
  • ANEURALNETWORKS_CONV_2D
  • ANEURALNETWORKS_ADD
Known issues
Support for Multiple Tensor inputs at runtime to model/network is ongoing
License
Android Neural Networks HAL is distributed under the Apache License, Version 2.0
You may obtain a copy of the License at: http://www.apache.org/licenses/LICENSE-2.0
Intel® Math Kernel Library for Deep Neural Networks (Intel® MKL-DNN) is an open source
performance library for Deep Learning (DL) applications intended for acceleration of DL
frameworks on Intel® architecture.
How to provide feedback
By default, please submit an issue using native github.com interface:
https://github.com/intel/nn-hal/issues
How to contribute
Create a pull request on github.com with your patch. Make sure your change is cleanly building
and passing ULTs.
A maintainer will contact you if there are questions or concerns.
Continuous Integration
Before committing any changes, make sure the coding style and testing configs are correct.
If not, the CI will fail.
Coding Style
Run the following command to ensure that the proper coding style is being followed:
 find . -regex '.*\.\(cpp\|hpp\|cc\|cxx\|h\)' -exec clang-format -style=file -i {} \;

Build and Test
Update the BOARD value in build-test.sh as per your test requirement.
If your BOARD is not supported, please contact the maintainer to get it added.
Currently, the CI builds the intel-nnhal package and runs the following tests:
  • Functional tests that include ml_cmdline and a subset of cts and vts tests.
About
No description, website, or topics provided.
Resources
Readme
Code of conduct
Code of conduct
Contributing
Contributing
Security policy
Security policy
Activity
Custom properties
Stars
46 stars
Watchers
4 watching
Forks
50 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content

This repository was archived by the owner on Jul 18, 2024. It is now read-only.

BranchesTags
Repository files navigation
DISCONTINUATION OF PROJECT
This project will no longer be maintained by Intel.

Intel has ceased development and contributions including, but not limited to, maintenance, bug fixes, new releases, or updates, to this project.

Intel no longer accepts patches to this project.

If you have an ongoing need to use this project, are interested in independently developing it, or would like to maintain patches for the open source software community, please create your own fork of this project.
CI
Android Neural Networks HAL with OpenVINO supporting hardware accelerators such as /
Intel® Math Kernel Library for Deep Neural Networks (Intel® MKL-DNN)
Introduction
The Android Neural Network Hardware Abstraction Layer(NN HAL) provides the hardware accelration
for Android Neural Networks (NN) API. Intel NN-HAL takes the advantage of the Intel MKLD-DNN,
enables high performance and low power implementation of Neural Networks API.
Intel MKL-DNN https://github.com/intel/mkl-dnn & https://01.org/mkl-dnn
Android NN API is on [Neural Networks API]
(https://developer.android.com/ndk/guides/neuralnetworks/index.html).
OpenVINO deep learning framework https://github.com/opencv/dldt & https://01.org/openvinotoolkit
Supported Operations
Following operations are currently supported by Android Neural Networks HAL for Intel MKL-DNN.
  • ANEURALNETWORKS_CONV_2D
  • ANEURALNETWORKS_ADD
Known issues
Support for Multiple Tensor inputs at runtime to model/network is ongoing
License
Android Neural Networks HAL is distributed under the Apache License, Version 2.0
You may obtain a copy of the License at: http://www.apache.org/licenses/LICENSE-2.0
Intel® Math Kernel Library for Deep Neural Networks (Intel® MKL-DNN) is an open source
performance library for Deep Learning (DL) applications intended for acceleration of DL
frameworks on Intel® architecture.
How to provide feedback
By default, please submit an issue using native github.com interface:
https://github.com/intel/nn-hal/issues
How to contribute
Create a pull request on github.com with your patch. Make sure your change is cleanly building
and passing ULTs.
A maintainer will contact you if there are questions or concerns.
Continuous Integration
Before committing any changes, make sure the coding style and testing configs are correct.
If not, the CI will fail.
Coding Style
Run the following command to ensure that the proper coding style is being followed:
 find . -regex '.*\.\(cpp\|hpp\|cc\|cxx\|h\)' -exec clang-format -style=file -i {} \;

Build and Test
Update the BOARD value in build-test.sh as per your test requirement.
If your BOARD is not supported, please contact the maintainer to get it added.
Currently, the CI builds the intel-nnhal package and runs the following tests:
  • Functional tests that include ml_cmdline and a subset of cts and vts tests.
About
No description, website, or topics provided.
Resources
Readme
Code of conduct
Code of conduct
Contributing
Contributing
Security policy
Security policy
Activity
Custom properties
Stars
46 stars
Watchers
4 watching
Forks
50 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content

This repository was archived by the owner on Jul 18, 2024. It is now read-only.

BranchesTags
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DISCONTINUATION OF PROJECT
This project will no longer be maintained by Intel.

Intel has ceased development and contributions including, but not limited to, maintenance, bug fixes, new releases, or updates, to this project.

Intel no longer accepts patches to this project.

If you have an ongoing need to use this project, are interested in independently developing it, or would like to maintain patches for the open source software community, please create your own fork of this project.
CI
Android Neural Networks HAL with OpenVINO supporting hardware accelerators such as /
Intel® Math Kernel Library for Deep Neural Networks (Intel® MKL-DNN)
Introduction
The Android Neural Network Hardware Abstraction Layer(NN HAL) provides the hardware accelration
for Android Neural Networks (NN) API. Intel NN-HAL takes the advantage of the Intel MKLD-DNN,
enables high performance and low power implementation of Neural Networks API.
Intel MKL-DNN https://github.com/intel/mkl-dnn & https://01.org/mkl-dnn
Android NN API is on [Neural Networks API]
(https://developer.android.com/ndk/guides/neuralnetworks/index.html).
OpenVINO deep learning framework https://github.com/opencv/dldt & https://01.org/openvinotoolkit
Supported Operations
Following operations are currently supported by Android Neural Networks HAL for Intel MKL-DNN.
  • ANEURALNETWORKS_CONV_2D
  • ANEURALNETWORKS_ADD
Known issues
Support for Multiple Tensor inputs at runtime to model/network is ongoing
License
Android Neural Networks HAL is distributed under the Apache License, Version 2.0
You may obtain a copy of the License at: http://www.apache.org/licenses/LICENSE-2.0
Intel® Math Kernel Library for Deep Neural Networks (Intel® MKL-DNN) is an open source
performance library for Deep Learning (DL) applications intended for acceleration of DL
frameworks on Intel® architecture.
How to provide feedback
By default, please submit an issue using native github.com interface:
https://github.com/intel/nn-hal/issues
How to contribute
Create a pull request on github.com with your patch. Make sure your change is cleanly building
and passing ULTs.
A maintainer will contact you if there are questions or concerns.
Continuous Integration
Before committing any changes, make sure the coding style and testing configs are correct.
If not, the CI will fail.
Coding Style
Run the following command to ensure that the proper coding style is being followed:
 find . -regex '.*\.\(cpp\|hpp\|cc\|cxx\|h\)' -exec clang-format -style=file -i {} \;

Build and Test
Update the BOARD value in build-test.sh as per your test requirement.
If your BOARD is not supported, please contact the maintainer to get it added.
Currently, the CI builds the intel-nnhal package and runs the following tests:
  • Functional tests that include ml_cmdline and a subset of cts and vts tests.
About
No description, website, or topics provided.
Resources
Readme
Code of conduct
Code of conduct
Contributing
Contributing
Security policy
Security policy
Activity
Custom properties
Stars
46 stars
Watchers
4 watching
Forks
50 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content

This repository was archived by the owner on Jul 18, 2024. It is now read-only.

BranchesTags
Repository files navigation
DISCONTINUATION OF PROJECT
This project will no longer be maintained by Intel.

Intel has ceased development and contributions including, but not limited to, maintenance, bug fixes, new releases, or updates, to this project.

Intel no longer accepts patches to this project.

If you have an ongoing need to use this project, are interested in independently developing it, or would like to maintain patches for the open source software community, please create your own fork of this project.
CI
Android Neural Networks HAL with OpenVINO supporting hardware accelerators such as /
Intel® Math Kernel Library for Deep Neural Networks (Intel® MKL-DNN)
Introduction
The Android Neural Network Hardware Abstraction Layer(NN HAL) provides the hardware accelration
for Android Neural Networks (NN) API. Intel NN-HAL takes the advantage of the Intel MKLD-DNN,
enables high performance and low power implementation of Neural Networks API.
Intel MKL-DNN https://github.com/intel/mkl-dnn & https://01.org/mkl-dnn
Android NN API is on [Neural Networks API]
(https://developer.android.com/ndk/guides/neuralnetworks/index.html).
OpenVINO deep learning framework https://github.com/opencv/dldt & https://01.org/openvinotoolkit
Supported Operations
Following operations are currently supported by Android Neural Networks HAL for Intel MKL-DNN.
  • ANEURALNETWORKS_CONV_2D
  • ANEURALNETWORKS_ADD
Known issues
Support for Multiple Tensor inputs at runtime to model/network is ongoing
License
Android Neural Networks HAL is distributed under the Apache License, Version 2.0
You may obtain a copy of the License at: http://www.apache.org/licenses/LICENSE-2.0
Intel® Math Kernel Library for Deep Neural Networks (Intel® MKL-DNN) is an open source
performance library for Deep Learning (DL) applications intended for acceleration of DL
frameworks on Intel® architecture.
How to provide feedback
By default, please submit an issue using native github.com interface:
https://github.com/intel/nn-hal/issues
How to contribute
Create a pull request on github.com with your patch. Make sure your change is cleanly building
and passing ULTs.
A maintainer will contact you if there are questions or concerns.
Continuous Integration
Before committing any changes, make sure the coding style and testing configs are correct.
If not, the CI will fail.
Coding Style
Run the following command to ensure that the proper coding style is being followed:
 find . -regex '.*\.\(cpp\|hpp\|cc\|cxx\|h\)' -exec clang-format -style=file -i {} \;

Build and Test
Update the BOARD value in build-test.sh as per your test requirement.
If your BOARD is not supported, please contact the maintainer to get it added.
Currently, the CI builds the intel-nnhal package and runs the following tests:
  • Functional tests that include ml_cmdline and a subset of cts and vts tests.
About
No description, website, or topics provided.
Resources
Readme
Code of conduct
Code of conduct
Contributing
Contributing
Security policy
Security policy
Activity
Custom properties
Stars
46 stars
Watchers
4 watching
Forks
50 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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DISCONTINUATION OF PROJECT
This project will no longer be maintained by Intel.

Intel has ceased development and contributions including, but not limited to, maintenance, bug fixes, new releases, or updates, to this project.

Intel no longer accepts patches to this project.

If you have an ongoing need to use this project, are interested in independently developing it, or would like to maintain patches for the open source software community, please create your own fork of this project.
CI
Android Neural Networks HAL with OpenVINO supporting hardware accelerators such as /
Intel® Math Kernel Library for Deep Neural Networks (Intel® MKL-DNN)
Introduction
The Android Neural Network Hardware Abstraction Layer(NN HAL) provides the hardware accelration
for Android Neural Networks (NN) API. Intel NN-HAL takes the advantage of the Intel MKLD-DNN,
enables high performance and low power implementation of Neural Networks API.
Intel MKL-DNN https://github.com/intel/mkl-dnn & https://01.org/mkl-dnn
Android NN API is on [Neural Networks API]
(https://developer.android.com/ndk/guides/neuralnetworks/index.html).
OpenVINO deep learning framework https://github.com/opencv/dldt & https://01.org/openvinotoolkit
Supported Operations
Following operations are currently supported by Android Neural Networks HAL for Intel MKL-DNN.
  • ANEURALNETWORKS_CONV_2D
  • ANEURALNETWORKS_ADD
Known issues
Support for Multiple Tensor inputs at runtime to model/network is ongoing
License
Android Neural Networks HAL is distributed under the Apache License, Version 2.0
You may obtain a copy of the License at: http://www.apache.org/licenses/LICENSE-2.0
Intel® Math Kernel Library for Deep Neural Networks (Intel® MKL-DNN) is an open source
performance library for Deep Learning (DL) applications intended for acceleration of DL
frameworks on Intel® architecture.
How to provide feedback
By default, please submit an issue using native github.com interface:
https://github.com/intel/nn-hal/issues
How to contribute
Create a pull request on github.com with your patch. Make sure your change is cleanly building
and passing ULTs.
A maintainer will contact you if there are questions or concerns.
Continuous Integration
Before committing any changes, make sure the coding style and testing configs are correct.
If not, the CI will fail.
Coding Style
Run the following command to ensure that the proper coding style is being followed:
 find . -regex '.*\.\(cpp\|hpp\|cc\|cxx\|h\)' -exec clang-format -style=file -i {} \;

Build and Test
Update the BOARD value in build-test.sh as per your test requirement.
If your BOARD is not supported, please contact the maintainer to get it added.
Currently, the CI builds the intel-nnhal package and runs the following tests:
  • Functional tests that include ml_cmdline and a subset of cts and vts tests.
About
No description, website, or topics provided.
Resources
Readme
Code of conduct
Code of conduct
Contributing
Contributing
Security policy
Security policy
Activity
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Stars
46 stars
Watchers
4 watching
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50 forks
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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Repository files navigation
DISCONTINUATION OF PROJECT
This project will no longer be maintained by Intel.

Intel has ceased development and contributions including, but not limited to, maintenance, bug fixes, new releases, or updates, to this project.

Intel no longer accepts patches to this project.

If you have an ongoing need to use this project, are interested in independently developing it, or would like to maintain patches for the open source software community, please create your own fork of this project.
CI
Android Neural Networks HAL with OpenVINO supporting hardware accelerators such as /
Intel® Math Kernel Library for Deep Neural Networks (Intel® MKL-DNN)
Introduction
The Android Neural Network Hardware Abstraction Layer(NN HAL) provides the hardware accelration
for Android Neural Networks (NN) API. Intel NN-HAL takes the advantage of the Intel MKLD-DNN,
enables high performance and low power implementation of Neural Networks API.
Intel MKL-DNN https://github.com/intel/mkl-dnn & https://01.org/mkl-dnn
Android NN API is on [Neural Networks API]
(https://developer.android.com/ndk/guides/neuralnetworks/index.html).
OpenVINO deep learning framework https://github.com/opencv/dldt & https://01.org/openvinotoolkit
Supported Operations
Following operations are currently supported by Android Neural Networks HAL for Intel MKL-DNN.
  • ANEURALNETWORKS_CONV_2D
  • ANEURALNETWORKS_ADD
Known issues
Support for Multiple Tensor inputs at runtime to model/network is ongoing
License
Android Neural Networks HAL is distributed under the Apache License, Version 2.0
You may obtain a copy of the License at: http://www.apache.org/licenses/LICENSE-2.0
Intel® Math Kernel Library for Deep Neural Networks (Intel® MKL-DNN) is an open source
performance library for Deep Learning (DL) applications intended for acceleration of DL
frameworks on Intel® architecture.
How to provide feedback
By default, please submit an issue using native github.com interface:
https://github.com/intel/nn-hal/issues
How to contribute
Create a pull request on github.com with your patch. Make sure your change is cleanly building
and passing ULTs.
A maintainer will contact you if there are questions or concerns.
Continuous Integration
Before committing any changes, make sure the coding style and testing configs are correct.
If not, the CI will fail.
Coding Style
Run the following command to ensure that the proper coding style is being followed:
 find . -regex '.*\.\(cpp\|hpp\|cc\|cxx\|h\)' -exec clang-format -style=file -i {} \;

Build and Test
Update the BOARD value in build-test.sh as per your test requirement.
If your BOARD is not supported, please contact the maintainer to get it added.
Currently, the CI builds the intel-nnhal package and runs the following tests:
  • Functional tests that include ml_cmdline and a subset of cts and vts tests.
About
No description, website, or topics provided.
Resources
Readme
Code of conduct
Code of conduct
Contributing
Contributing
Security policy
Security policy
Activity
Custom properties
Stars
46 stars
Watchers
4 watching
Forks
50 forks
Report repository
Releases
Packages
Used by
Contributors
Languages