Axelera AIis in the AI Infrastructure Industry

Axelera AI delivers an AI-native game-changing hardware and software platform to accelerate artificial intelligence. Headquartered in the AI Innovation Center of the High Tech Campus in Eindhoven, Axelera AI has R&D offices in Belgium, Switzerland, UK and Italy and operations in 15 European countries . Its team of experts in AI software and hardware hail from top AI firms and Fortune 500 companies.

Axelera AI

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Entendiendo PyTorch: las bases de las bases para hacer inteligencia artificial

Since the 5 years ago, I’ve been working remotely and loving it. I’m now the founder of , an online school for remote software developers where we teach our students how to take advantage of opportunities regardless of where they are. acquisition of my previous startup Microverse This guide is a living collection of resources that I often use or recommend to other people who want to become remote workers. If you have anything that you want to add or update, please leave a comment. Conferences (May 31-June 4, San Marcos La Laguna, Guatemala) Rebel + Connect’s Remote Work Summit (June 23-24, Bali, Indonesia) Running Remote (Sep 8-10, Lisbon, Portugal) DNX Conference (October, Medellín, Colombia) 7in7 (Date TBD, Gran Canaria, Spain) Nomad City (TBD) TRaD Works Forum (TBD) Yonder Conference (Jan 19, 2019) Nomad Summit Remote Working Conference (Feb 9-11 2018, Penang, Malaysia) The Coworking Unconference Asia (March 5 2018, Online) The Remote Work Summit (March 21–22 2018, Austin TX) Scatter (May 39, 2018, Online) Out of Office Companies 10up Aha! Articulate Auth0 Automattic Axelerant Balsamiq Bandcamp Basecamp Buffer Canonical Chef Clevertech Cloudbees Crossover DataRobot DataStax DigitalOcean Doist DuckDuckGo Elastic Envato Fire Engine Red Four Kitchens Genuitec Ghost GitHub GitLab Gun.io Heap Help Scout Heroku inVision Jackson River Jitbit Software Lullabot Mobile Jazz Mozilla Olark onTheGoSystems O’Reilly Media Original Eight Parse.ly Particular Software Pythian Scrapinghub Simple A SitePen Sonatype Spreaker Stack Exchange Toggl Toptal Trello Universal Mind X-team X-team Not enough? Check out this spreadsheet with more than other 600 companies hiring remotely: _Edit description_docs.google.com 600+ Startups hiring Remotely in 2018 - by Remotive.io Communities Nomad Base Reddit/telecommuting Remotive Hacker Paradise Remote Year Remote Way #freelance YonderWork Be Unsettled Nomad House DNX Camp Reboot Life Flylancers Co-living Nomad.Life Roam WeLive WifiTribe Outsite CoBoat NomadCruise Job boards (Remote filter) AngelList FlexJobs Jobbatical Jobspresso NODESK RemoteBase Remote.co Remote | OK Remotive Remote Friendly Stack Overflow remote job listings WeWorkRemotely WFH.io Workaline Working Nomads Newsletters Remotive Remoteur Books , by Jason Fried and David Heinemeier Hansson (37signals) Remote: Office Not Required , by Wade Foster (Zapier) The Ultimate Guide to Remote Work , by Scott Berkun The Year Without Pants: WordPress.com and the Future of Work , by Coby Chapple (GitHub) The Remote Jobseeker’s Handbook , by Robert Pankowecki and Andrzej Krzywda (Arkency) Async Remote (Wrike) The Art of Staying Productive Even Across Distance (Hubstaff) No Excuses, A definitive guide to building a remote team , by Dr. David Rock Your Brain at Work Working Remotely — The Telecommuter’s Guide to the Galaxy , Rules for Focused Success in a Distracted World Deep Work Podcasts Free People Move Podcast Lessons from Distributed Companies REMOTE WORKS Yonder Podcast Wide Teams Tools Communication Fleep Flowdock Hipchat Mumble PukkaTeam Sneek.io Rocket.Chat Slack Twist Video conferencing Appear.in Bluejeans Hangouts Skype Zoom Programming Codeshare.io Floobits Time tracking Clockify Harvest Toggl Project management Asana Trello Basecamp Pivotal Tracker Timezone management Timezone.io Spacetime Sundial Do you want to add anything? Leave a comment :)

The Ultimate List of Resources for Remote Workers

Integral Neural Networks (INNs) are flexible architectures that trained once can be transformed to arbitrary user-defined size without any fine-tuning. As sound waves (music) can be sampled at any desired sampling rate (sound quality) INNs can change data and parameters shape (DNN quality) dynamically. The team of presented their paper, “Integral Neural Networks,” at the IEEE/CVF CVPR 2023 conference. This work was recognized as one of only 12 ‘Award Candidate’ papers featured at the event. INNs are a new class of neural networks that combine continuous parameters and integral operators to represent basic layers. During the inference stage, INNs convert to a vanilla DNN representation by discrete sampling from continuous weights. Parameters of such networks are continuous along filter and channel dimensions which leads to structured pruning without fine-tuning just by re-discretization along those dimensions. TheStage.ai In this article, we will guide you through the process of transforming a 4x image super-resolution EDSR model to an INN, then show how to achieve structured pruning of the model. Finally, we will convert the INN back to a discrete DNN and deploy it on an Nvidia GPU for efficient inference. This article will proceed as follows: A brief introduction to INNs. Overview of EDSR network for super-resolution task. TorchIntegral framework application to obtain integral EDSR in a single line of code. Structural pruning of INN without INN fine-tuning (fast pipeline). Deploy pruned models on Nvidia GPUs. For further information and updates, please check the following resources: INN project site INN project Github Support code of this article INNs for DNNs pruning without fine-tuning Layers in INNs are replaced by integral operators, but the practical evaluation of integral operators requires the discretization of input signals for the usage of numerical integration methods. It turns out that the layers in INNs are designed in such a way as to coincide with classical DNN layers (fully-connected, convolutions) after discretization. EDSR 4x Image Super-Resolution pruning Image super-resolution is a well-known computer vision task where an image should be enhanced with a known or unknown degradation operator. We consider classical super-resolution form using bicubic downsampling as a degradation operator. Many architectures can be used for image super-resolution tasks, including high-end neural networks based on diffusion models and transformers. In this document, we will focus on the 4x EDSR architecture. EDSR architecture is well-suited for our demonstration as it comprises ResNet (which is widely used in many deep learning problems) and a 4x upsampling block at the end. A schematic description of EDSR can be found in the following figures. Specifics of EDSR architecture pruning Structured pruning, which involves deleting entire filters or channels, has unique implications for residual blocks, which serve as the primary building blocks in EDSR. Since each state is updated by adding a Conv -> ReLU -> Conv block to the input, the input and output signals must have the same number of parameters. This can be efficiently managed in TorchIntegral by creating a . The figure below illustrates that the second convolution of each residual block forms a single group. pruning dependency graph To prune the second convolution in a residual block, it is necessary to prune all second convolutions in each residual block. However, for a more flexible setup, we prune filters of the first convolutions, and therefore channels of the second convolutions, in all residual blocks. Conversion of EDSR model to INN EDSR For conversions from the pre-trained DNNs, we utilize our special filter-channels permutation algorithm with further smooth interpolation. The permutation algorithm preserves model quality while making the weights of the DNNs look like sampled from continuous functions. import torch
import torchintegral as inn
from super_image import EdsrModel

# creating 4x EDSR model
model = EdsrModel.from_pretrained("eugenesiow/edsr", scale=4).cuda()

# Transform model layers to integral.
# continous_dims and discrete dims define which dimensions
# of parameters tensors should be parametrized continuously
# or stay fixed size as in discrete networks.
# In our case we make all filter and channel dimensions
# to be continuous excluding convolutions of the upsample block.
model = inn.IntegralWrapper(init_from_discrete=True)(
    model, example_input, continuous_dims, discrete_dims
).cuda() Integration grid tuning: structured post-training pruning of DNNs Integration grid tuning is the operation that selects smoothly (under SGD optimization) parameter tensors whose filters should be sampled for user-defined numbers. Unlike the filter/channel deletion methods, INNs generate filters that can combine several discrete filters because of interpolation operation. INNs introduce on the tensor of parameters along filter and channel dimensions. soft select-by-index operation # Set trainable gird for each integral layer
# Each group should have the same grid
# During the sum of continuous signals
# We need to sample it using the same set of points

for group in model.groups:
    new_size = 224 if 'operator' in group.operations else 128 
    group.reset_grid(inn.TrainableGrid1D(new_size))

# Prepare model for tuning of integration grid
model.grid_tuning()
# Start training
train(model, train_data, test_data) Integration grid tuning is a that can be carried out on a small calibration set. The result of this optimization is a structurally compressed DNN. Tests on a single Nvidia A4000 show that integration grid tuning on the full Div2k dataset . A distributed setup on 4x A4000 demonstrates almost a 4x speedup, resulting in an optimization time of . fast optimization process requires 4 minutes just 1 minute During our experiments, we found that 500 images give the same result as a full train set Div2k of 4000 images. Performance The resulting INN model can be easily converted to a discrete model and deployed on any NVIDIA GPU. We provide frames per second (FPS) on the RTX A4000 with an input resolution of 64x64. The compressed model achieves almost a 2x speedup. To convert the pruned INN model to a discrete model the following line of code can be used: model = model.transform_to_discrete()
# then model can be compiled, for instance
# compilation can add an additional 1.4x speedup for inference
model = torch.compile(model, backend='cudagraphs') Model Size FP16 FPS RTX A4000 PSNR EDSR orig. 75 MB 170 30.65 INN EDSR 30% 52 MB 230 30.43 INN EDSR 40% 45 MB 270 30.34 INN EDSR 50% 37 MB 320 30.25 Conclusion In this article, we have presented an overview of the CVPR2023 award candidate paper, “Integral Neural Networks”. It was applied for the post-training pruning of the 4x EDSR model, achieving nearly a 2x speedup with a single line of code and 1-minute fine-tuning of the integration grid. In our future articles, we will present more applications of the INNs and will cover more details on efficient model deployment. Stay tuned: INN project site INN project Github Support code of this article Thank you for your attention! Also published . here

Unleashing 2x Acceleration for DNNs: Transforming Models with Integral Neural Networks in Just 1 Min

DNN-এর জন্য 2x ত্বরণ আনলিশ করা: মাত্র 1 মিনিটে ইন্টিগ্রাল নিউরাল নেটওয়ার্ক সহ মডেলগুলিকে রূপান্তর করা

Entfesseln Sie die doppelte Beschleunigung für DNNs: Transformieren Sie Modelle mit integralen neuronalen Netzen in nur 1 Minute

Liberando una aceleración 2x para DNN: transformando modelos con redes neuronales integrales en solo 1 minuto

Libérer une accélération 2x pour les DNN : transformer des modèles avec des réseaux de neurones intégraux en seulement 1 minute

डीएनएन के लिए 2x एक्सेलेरेशन जारी करना: केवल 1 मिनट में इंटीग्रल न्यूरल नेटवर्क के साथ मॉडल को बदलना

DNN の 2 倍の高速化を解き放つ: 統合ニューラル ネットワークを使用してわずか 1 分でモデルを変換

DNN을 위한 2배 가속 실현: 단 1분 만에 통합 신경망을 사용하여 모델 변환

Liberando aceleração 2x para DNNs: transformando modelos com redes neurais integrais em apenas 1 minuto

DNN'ler için 2 Kat Hızlanmayı Ortaya Çıkarma: Sadece 1 Dakikada Entegre Sinir Ağlarıyla Modelleri Dönüştürme

Giải phóng khả năng tăng tốc gấp 2 lần cho DNN: Chuyển đổi mô hình với Mạng thần kinh tích hợp chỉ trong 1 phút

为 DNN 释放 2 倍加速：只需 1 分钟即可使用集成神经网络转换模型

In this article, I want to tell you about a useful plugin ADB Idea. I use it every day, but I noticed that others use it in their work quite rarely. Working With ADB Idea The plugin is off by default. After adding it, the toolbars will appear: All this plugin does is replace the standard ADB commands. For example, the is analogous to the terminal command. The advantage of ADB Idea is that the most frequently used commands are placed in the toolbar and you don't have to remember them all the time. ADB Kill App ADB shell am kill Start the app. Click on the command, and verify that the application has closed and disappeared from the recently used stack. ADB Kill App You can optimize even more. Since the commands are present in the panel, you can assign keyboard shortcuts to them, just like any other, which will also speed up development. Let's go into the Keymap settings: In the search box, type ADB and you will see the necessary commands. Find the previously described and assign a key combination to it, such as . Click OK. Close the menu, launch the app, and press . We will make sure that the application is closed. ADB Kill App Option+K Option+K Creating an ADB quick list There are quite a lot of useful adb commands. So, we run the risk of cramming all the key combinations. The way out of this is to create a quick list. Let's go to Preferences > Appearance & Behavior > Quick Lists: Create a new Quick List (point 1). Give it a name (point 2). Fill it with the commands we need (point 3). Go to Keymap, find Quick Lists, and then the adb we created and assign a key combination to it. In my case, it's . Let's press OK: Launch the application again and press and fast 4. Make sure that the application is closed. Option+F2 Option+F2 You can read more about the plugin here: https://plugins.jetbrains.com/plugin/7380-adb-idea https://github.com/pbreault/adb-idea Conclusion As you can see, a small ADB Idea plugin can speed up development considerably. You don't need to type long commands in the terminal and memorize them. You can set keyboard shortcuts on the commands and enter them into the . In my experience, there are a few disadvantages to ADB Idea: Quick list When updating the Android Studio, the plugin does not always have time to quickly update and is sometimes no longer compatible. Sometimes the plugin stops attaching to the application process. There is no possibility of creating custom more complex commands.
However, despite all these disadvantages, ADB Idea still greatly speeds up application development.

Accelerating Android Application Development With the ADB Idea Plugin

Acelerar el desarrollo de aplicaciones de Android con el complemento ADB Idea

Accélérer le développement d'applications Android avec le plugin ADB Idea

एडीबी आइडिया प्लगइन के साथ एंड्रॉइड एप्लिकेशन डेवलपमेंट को तेज करना

ADB Idea プラグインで Android アプリケーション開発を加速

Acelerando o desenvolvimento de aplicativos Android com o plug-in ADB Idea

Tăng tốc phát triển ứng dụng Android với plugin ADB Idea

使用 ADB Idea 插件加速 Android 应用程序开发

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