Palestra focada em visão computacional, com o intento de demonstrar a necessidade e importância da aritmética matricial
para Deep Learning e resoluções de problemas na área utilizando TensorFlow e CNTK.
Open the door of embedded systems to IoT! mruby on LEGO Mindstorms (R)Takehiko YOSHIDA
mruby is lightweight implementation of the Ruby language, and it will encourage embedded system programmers to open the door to IoT (Internet of Things).
Tiny ML for spark Fun Edge
https://www.ittraining.com.tw/ittraining/it-elearning/el-ai/ai-tensorflow-lite-for-mcu
TensorFlow Lite for MCU正是專為邊緣裝置設計的TensorFlow模型預測框架,是TensorFlow的精簡版本,讓開發者可以在物聯網與嵌入式裝置中部署微型機器學習模型。 本課程將教授AI模型如何佈署於微控制器,包含模型訓練、模型最佳化以及TensorFlow Lite框架的程式開發等。此外,在實作上以Sparkfun edge board (ARM cortex M4)為例,說明如何以TensorFlow Lite 進行微控制器上面的人工智慧開發專案,包含人臉偵測、關鍵字的字詞偵測、姿態識別、異常偵測等。
Open the door of embedded systems to IoT! mruby on LEGO Mindstorms (R)Takehiko YOSHIDA
mruby is lightweight implementation of the Ruby language, and it will encourage embedded system programmers to open the door to IoT (Internet of Things).
Tiny ML for spark Fun Edge
https://www.ittraining.com.tw/ittraining/it-elearning/el-ai/ai-tensorflow-lite-for-mcu
TensorFlow Lite for MCU正是專為邊緣裝置設計的TensorFlow模型預測框架,是TensorFlow的精簡版本,讓開發者可以在物聯網與嵌入式裝置中部署微型機器學習模型。 本課程將教授AI模型如何佈署於微控制器,包含模型訓練、模型最佳化以及TensorFlow Lite框架的程式開發等。此外,在實作上以Sparkfun edge board (ARM cortex M4)為例,說明如何以TensorFlow Lite 進行微控制器上面的人工智慧開發專案,包含人臉偵測、關鍵字的字詞偵測、姿態識別、異常偵測等。
The presentation in great details explains how modern CPUs work, what is instruction pipeline, cache, superscalar CPUs, out of order execution, speculative execution, branch misses and what is branch predictor.
Then the presentation explains step-by-step how the spectre attack works and why it was possible.
Next it touches process isolation, system calls and privilege levels, explains step-by-step how the meltdown attack works and why it was possible.
At the very end there is a source code for Spectre-based Meltdown attack (i.e. 2-in-1) in just 99 lines of code.
De gemiddelde GPU bevat tegenwoordig meer PK's dan de CPU. Naar aanleiding hiervan komen er steeds meer mogelijkheden om computationele problemen te verplaatsen van de CPU naar de GPU. Deze presentatie zal een inleiding zijn hoe je dit in Java kunt doen met behulp van Jogamp JoCL. Aan de hand van enkele simpele problemen wordt aangetoond wanneer een GPU beter ingezet kan worden dan een CPU en vice versa. Dit is ook een van de speerpunten in Java 9 (Project Sumatra) wat o.a. JoCL als inspiratie gebruikt.
Accelerated Training of Transformer ModelsDatabricks
Language models help in automating a wide range of natural language processing (NLP) tasks such as speech recognition, machine translation, text summarization and more. Transformer architecture was introduced a few years back and it has significantly changed the NLP landscape since then. Transformer based models are getting bigger and better to improve the state of the art on language understanding and generation tasks.
We all make mistakes while programming and spend a lot of time fixing them.
One of the methods which allows for quick detection of defects is source code static analysis.
We all make mistakes while programming and spend a lot of time fixing them.
One of the methods which allows for quick detection of defects is source code static analysis.
Accelerating Real Time Video Analytics on a Heterogenous CPU + FPGA PlatformDatabricks
The current uptrend in faster computational power has led to a more mature eco-system for image processing and video analytics. By using deep neural networks for image recognition and object detection we can achieve better than human accuracies. Industrial sectors led by retail and finance want to take advantage of these latest developments in real-time analysis of video content for fraud detection, surveillance and many other applications.
There are a couple of challenges involved in the real word implementation of a video analytics solution:
1) Most video analytics use-cases are effective only when response times are in milliseconds. Requirement of performing at very low latencies gives rise to a need for software and hardware acceleration
2) Such solutions will need wide-spread deployment and are expected to have low TCO. To address these two key challenges we propose a video analytics solution leveraging Spark Structured Streaming + DL framework (like Intel’s Analytics-Zoo & Tensorflow) built on a heterogenous CPU + FPGA hardware platform.
The proposed solution provides >3x acceleration in performance to a video analytics pipeline when compared to a CPU only implementation while requiring zero code change on the application side as well as achieving more than 2x decrease in TCO. Our video analytics pipeline includes ingestion of video stream + H.264 decode to image frames + image transformation + image inferencing, that uses a deep neural network. FPGA based solution offloads the entire pipeline computation to the FPGA while CPU only solution implements the pipeline using OpenCV + Spark Structured Streaming + Intel’s Analytics-Zoo DL library.
Key Take aways:
1. Optimizing performance of Spark Streaming + DL pipeline
2. Acceleration of video analytics pipeline using FPGA to deliver high throughput at low latency and reduced TCO.
3. Performance data for benchmarking CPU and CPU + FPGA based solution.
Mastering Multiplayer Stage3d and AIR game development for mobile devicesJean-Philippe Doiron
Video Presentation : http://tv.adobe.com/watch/max-2013/mastering-multiplayer-stage3d-and-air-game-development-for-mobile-devices/
• The use of Stage3D across web and mobile deployments (with Adobe AIR) .
• The challenges encountered when attempting to maintain high-performance specifications on mobile devices .
• Being agile in a pre production game development
• We'll show how we have jump our of the predefined sandbox to develop creative solution on well known problem.
Accelerating microbiome research with OpenACCIgor Sfiligoi
Presented at OpenACC Summit 2020.
UniFrac is a commonly used metric in microbiome research for comparing microbiome profiles to one another. Computing UniFrac on modest sample sizes used to take a workday on a server class CPU-only node, while modern datasets would require a large compute cluster to be feasible. After porting to GPUs using OpenACC, the compute of the same modest sample size now takes only a few minutes on a single NVIDIA V100 GPU, while modern datasets can be processed on a single GPU in hours. The OpenACC programming model made the porting of the code to GPUs extremely simple; the first prototype was completed in just over a day. Getting full performance did however take much longer, since proper memory access is fundamental for this application.
Nesta apresentação vermos o que é Visão Computacional, seus objetivos, aplicações e por que este é um problema inverso e mal posto. Vamos verificar de maneira prática como Visão Computacional pode ser complexa, e o quão sua utilização pode ser benéfica.
The presentation in great details explains how modern CPUs work, what is instruction pipeline, cache, superscalar CPUs, out of order execution, speculative execution, branch misses and what is branch predictor.
Then the presentation explains step-by-step how the spectre attack works and why it was possible.
Next it touches process isolation, system calls and privilege levels, explains step-by-step how the meltdown attack works and why it was possible.
At the very end there is a source code for Spectre-based Meltdown attack (i.e. 2-in-1) in just 99 lines of code.
De gemiddelde GPU bevat tegenwoordig meer PK's dan de CPU. Naar aanleiding hiervan komen er steeds meer mogelijkheden om computationele problemen te verplaatsen van de CPU naar de GPU. Deze presentatie zal een inleiding zijn hoe je dit in Java kunt doen met behulp van Jogamp JoCL. Aan de hand van enkele simpele problemen wordt aangetoond wanneer een GPU beter ingezet kan worden dan een CPU en vice versa. Dit is ook een van de speerpunten in Java 9 (Project Sumatra) wat o.a. JoCL als inspiratie gebruikt.
Accelerated Training of Transformer ModelsDatabricks
Language models help in automating a wide range of natural language processing (NLP) tasks such as speech recognition, machine translation, text summarization and more. Transformer architecture was introduced a few years back and it has significantly changed the NLP landscape since then. Transformer based models are getting bigger and better to improve the state of the art on language understanding and generation tasks.
We all make mistakes while programming and spend a lot of time fixing them.
One of the methods which allows for quick detection of defects is source code static analysis.
We all make mistakes while programming and spend a lot of time fixing them.
One of the methods which allows for quick detection of defects is source code static analysis.
Accelerating Real Time Video Analytics on a Heterogenous CPU + FPGA PlatformDatabricks
The current uptrend in faster computational power has led to a more mature eco-system for image processing and video analytics. By using deep neural networks for image recognition and object detection we can achieve better than human accuracies. Industrial sectors led by retail and finance want to take advantage of these latest developments in real-time analysis of video content for fraud detection, surveillance and many other applications.
There are a couple of challenges involved in the real word implementation of a video analytics solution:
1) Most video analytics use-cases are effective only when response times are in milliseconds. Requirement of performing at very low latencies gives rise to a need for software and hardware acceleration
2) Such solutions will need wide-spread deployment and are expected to have low TCO. To address these two key challenges we propose a video analytics solution leveraging Spark Structured Streaming + DL framework (like Intel’s Analytics-Zoo & Tensorflow) built on a heterogenous CPU + FPGA hardware platform.
The proposed solution provides >3x acceleration in performance to a video analytics pipeline when compared to a CPU only implementation while requiring zero code change on the application side as well as achieving more than 2x decrease in TCO. Our video analytics pipeline includes ingestion of video stream + H.264 decode to image frames + image transformation + image inferencing, that uses a deep neural network. FPGA based solution offloads the entire pipeline computation to the FPGA while CPU only solution implements the pipeline using OpenCV + Spark Structured Streaming + Intel’s Analytics-Zoo DL library.
Key Take aways:
1. Optimizing performance of Spark Streaming + DL pipeline
2. Acceleration of video analytics pipeline using FPGA to deliver high throughput at low latency and reduced TCO.
3. Performance data for benchmarking CPU and CPU + FPGA based solution.
Mastering Multiplayer Stage3d and AIR game development for mobile devicesJean-Philippe Doiron
Video Presentation : http://tv.adobe.com/watch/max-2013/mastering-multiplayer-stage3d-and-air-game-development-for-mobile-devices/
• The use of Stage3D across web and mobile deployments (with Adobe AIR) .
• The challenges encountered when attempting to maintain high-performance specifications on mobile devices .
• Being agile in a pre production game development
• We'll show how we have jump our of the predefined sandbox to develop creative solution on well known problem.
Accelerating microbiome research with OpenACCIgor Sfiligoi
Presented at OpenACC Summit 2020.
UniFrac is a commonly used metric in microbiome research for comparing microbiome profiles to one another. Computing UniFrac on modest sample sizes used to take a workday on a server class CPU-only node, while modern datasets would require a large compute cluster to be feasible. After porting to GPUs using OpenACC, the compute of the same modest sample size now takes only a few minutes on a single NVIDIA V100 GPU, while modern datasets can be processed on a single GPU in hours. The OpenACC programming model made the porting of the code to GPUs extremely simple; the first prototype was completed in just over a day. Getting full performance did however take much longer, since proper memory access is fundamental for this application.
Similar to Deep Learning based on Computer Vision (20)
Nesta apresentação vermos o que é Visão Computacional, seus objetivos, aplicações e por que este é um problema inverso e mal posto. Vamos verificar de maneira prática como Visão Computacional pode ser complexa, e o quão sua utilização pode ser benéfica.
Como ensinar uma máquina a escrever com Deep Learning, CNTK, TensorFlow e AzureVitor Meriat
Se utilizando de redes GANs, nesta apresentação mostrei como criar imagens com base em um conjunto de imagens pré selecionadas se utilizando de deep learning.
Azure Stack - O poder da nuvem em seu datacenterVitor Meriat
Dia 07/05 na seda da Microsoft no Brasil, aconteceu a primeira edição do DevOps Summit Brasil. Esse é um evento que teve como origem a fusão dos já consagrados Azure Summit Brasil e ALM Summit Brasil com foco em promover o conhecimento de Cloud Computing e DevOps.
Dispositivos Inteligentes com Computação Cognitiva e IAVitor Meriat
Segundo Fábio Gandour, cientista-chefe da IBM Brasil, "Se existe algo mais importante que o conhecimento, é saber como usar esse conhecimento". Inteligência artificial é um assunto que já permeia a curiosidade humana nos últimos 50 anos, e agora temos ouvido falar sobre computação cognitiva que promete guiar a nova era da tecnologia da informação.
Computação Congnitiva embora seja um tema recente, já provou ter um grande potencial. A geração de conhecimento por meio de aprendizado e dos dados disponíveis é a próxima grande onda de inovação no mercado de tecnologia. Empresas usando feedbacks e dados de ambiente de lojas para traçar e explorar os padrões de consumo dos clientes, hospitais usando cognição para acelerar pesquisas contra o câncer, cidades controlando melhor os seus recursos são pequenos exemplos do que já temos no mundo conectado de hoje.
Ao unir computação cognitiva à Internet das Coisas conseguimos integrar e trabalhar um volume absurdo de dados com a inteligência cognitiva, gerando o subsídio necessário para as melhores tomadas de decisões.
Nesta sessão você vai conhecer os conceitos, as principais tecnologias envolvidas e como esse novo paradigma se encaixa no IoT atravéz dos dispositivos inteligentes.
QCon SP 2016 – Medição da experiência real dos usuários com sensores e Machin...Vitor Meriat
O uso de dispositivos nos veículos capturando dados forneceu uma visão clara sobre o que realmente acontecia nas caronas – e a análise dos dados com técnicas de machine learning permitiu reduzir distorções causadas por efeitos externos às caronas em si. Ao final, foi possível medir com maior precisão os múltiplos aspectos da experiência na utilização do serviço.
Vou apresentar nessa palestra técnicas de IoT e Inteligência Artificial foram fundamentais para melhorar a aplicação e a experiência dos usuários. Serão abordados os passos para construção da aplicação, sua arquitetura, utilização de sensores, cloud, analytics e aprendizado de máquina, assim como critérios de performance e custo computacional. Serão abordados também desafios enfrentados na comunicação entre os dispositivos utilizados nos veículos.
Internet das Coisas é possivelmente o termo mais falado no mundo da tecnologia nos últimos tempos, embora muito difundido, poucos realmente tratam IoT de início a fim, as vezes negligenciando a inteligência da nuvem, outras vezes segurança e focando quase sempre no hardware e seus componentes. Neste workshop venha ver de perto um dispositivo completo, funcionando e com todas as integrações. você poderá trabalhar com códigos para controlar os dispositivos e também com os dados gerados. A ideia é começarmos neste local algo bem maior que somente acender um led ou controlar algo remotamente. Venha conhecer e se divertir com o mundo da Internet das coisas. Serão abordados MQTT, conectividade e integração de sistemas e sensores com a nuvem, geração e tratamento de informações além do hardware. Não pretendemos montar hardware no workshop e sim utiliza-los para gerar uma solução.
TDC 2015 SP - O ciclo de vida de aplicações UWP Vitor Meriat
Apresentação realizada no dia 22-07-2015 para o TDC 2015 edição São Paulo.
http://www.thedevelopersconference.com.br/tdc/2015/saopaulo/trilha-universal-windows
Nuvem? Análise de dados e outros bichosVitor Meriat
Apresentação realizada no dia 30/05/2015 para o IoT Weekend Recife. http://goo.gl/dfP4QI
Além de uma visão sobre computação em nuvem, nesta seção você conhecerá um pouco mais sobre os serviços com foco em IOT do Microsoft AZURE. Os dados gerados por dispositivos vem em formatos diferentes e como podemos gerar uma análise desta informação e como isso pode ser um diferencial nas soluções.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
29. FPGAs
EVALUATION
CPUs and FPGAs,
ASICs under investigation
EFFICIENCY
TRAINING
CPUs and GPUs, limited FPGAs,
ASICs under investigation
Control
Unit
(CU)
Registers
Arithmetic
Logic Unit
(ALU)
+
+
+
+
+
+
+
FLEXIBILITY
CPUs GPUs
ASICs
36. Nc
FP32/64
TRAINING
A/D
SERIES
AZURE
BATCH
ND2
INT32/64
TRAINING
ND1
INT32
TRAINING
INFINIBAND HIGH SPEED NETWORK
FPGA1
ACCELERATED
APPS
FPGA
MICROSERVICES
HAAS
MGMT
Rendering Service
HPC Azure AI Training Service • What is it?
• Easy to run parallel large-scale training with GPU
and InfiniBand high speed networking
• Experimentation and simulation with any
framework
• Built on Azure Batch, Docker & Python
• Azure Compliant Infrastructure
• Designed with and supported by AIR Philly Team
•
• Key Capabilities
• Open- TensorFlow, CNTK, Caffe, MXNet support
• Submit and go parameterized job configuration
• Experiment management and monitoring
• Support teams with sharing of reserved capacity
• Re-image VMs between users for compliance
• Python API to integrate with experimentation tools
• Publish models for eval with AML
• H1 CY 2017
• Preview mid semester, summer GA –In Preview
• Good support for internal and external early
adopters
AZURE BATCH
DEEP LEARNING JOB SERVICE