"Implementing the TensorFlow Deep Learning Framework on Qualcomm’s Low-power DSP," a Presentation from Google
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TensorFlow has been implemented on Qualcomm's low-power DSP to run neural networks for machine learning. Models run 8 times faster and use 1.4 watts of power on the Snapdragon 820 processor's Hexagon DSP compared to 5 watts on a CPU. Qualcomm tested gemmlowp on the HVX and achieved over 5 times the speed and much lower power usage than a CPU. End to end performance on InceptionV1 was around 90 ms using the HVX versus 700 ms on a CPU.
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In this video from the Stanford HPC Conference, Liran Zvibel from Weka.IO presents: Making Machine Learning Compute Bound Again.
"GPUs are getting faster on a yearly cycle. Networking was able to catch up and support linear scaling of models that fit in memory. Traditional storage has not caught up to the condensed performance needed by GPU-filled servers. The amount of concurrent clients and the sheer amount of data required to effectively scale modern deep learning models keeps growing.
We are going to present WekaIO, the lowest latency, highest throughput file system solution that scales to 100s of PB in a single namespace supporting the most challenging deep learning projects that run today. We will present real life benchmarks comparing WekaIO performance to a local SSD file system, showing that we are the only coherent shared storage that is even faster than the current caching solutons, while allowing customers to linearly scale performance by adding more GPU servers. Also, we will view the complete ML project lifecycle, from collecting data, cleaning, tagging, exploring, training, validating, and finally archiving, and how customers can use cloud bursting to leverage public cloud infrastructure for improved economics."
Learn more: https://weka.io
and
http://hpcadvisorycouncil.com
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Very large scale distributed deep learning on BigDL
Presentation on how to bring Deep Learning To Big Data Platform.
If you like what you read be sure you ♥ it below. Thank you!
Welcome to the 2018 Stanford HPC Conference
In this deck, Gilad Shainer from the HPC Advisory Council kicks off the 2018 Stanford HPC Conference.
Watch the video: https://youtu.be/2ya9Ougcz9c
Learn more:
http://hpcadvisorycouncil.com
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Distributed Models Over Distributed Data with MLflow, Pyspark, and Pandas
Does more data always improve ML models? Is it better to use distributed ML instead of single node ML?
In this talk I will show that while more data often improves DL models in high variance problem spaces (with semi or unstructured data) such as NLP, image, video more data does not significantly improve high bias problem spaces where traditional ML is more appropriate. Additionally, even in the deep learning domain, single node models can still outperform distributed models via transfer learning.
Data scientists have pain points running many models in parallel automating the experimental set up. Getting others (especially analysts) within an organization to use their models Databricks solves these problems using pandas udfs, ml runtime and MLflow.
Self driving computers active learning workflows with human interpretable ve...
Human in the loop learning workflows leveraging deep learning to group and cluster data. Also, techniques for accounting for machine learning failures.
The Pandemic Changes Everything, the Need for Speed and Resiliency
Data Orchestration Summit 2020 organized by Alluxio
https://www.alluxio.io/data-orchestration-summit-2020/
The Pandemic Changes Everything, the Need for Speed and Resiliency
Parviz Peiravi, Global CTO of Financial Services Solutions, Intel
About Alluxio: alluxio.io
Engage with the open source community on slack: alluxio.io/slack
Video Analytics on Hadoop webinar victor fang-201309
Video Analytics on Hadoop webinar.
Presented by Pivotal Data Science Team 201309.
Industrial production process visualization with the Elastic Stack in real-ti...
Learn how the Mayr-Melnhof Group implemented production process visualization in a highly automated and fragmented industrial, process-control environment with the Elastic Stack.
IBM Middle East Data Science Connect 2016 - Doha, Qatar
IBM Middle East Data Science Connect 2016 - Doha, Qatar
Cloud Computing Was Built for Web Developers—What Does v2 Look Like for Deep...
What we call the public cloud was developed primarily to manage and deploy web servers. The target audience for these products is Dev Ops. While this is a massive and exciting market, the world of Data Science and Deep Learning is very different — and possibly even bigger. Unfortunately, the tools available today are not designed for this new audience and the cloud needs to evolve. This talk would cover what the next 10 years of cloud computing will look like.
Data Tells the Story - Greenplum Summit 2018
Data Tells the Story
Greenplum Summit at PostgresConf US 2018
Scott Yara
Apache SystemML - Declarative Large-Scale Machine Learning
Apache SystemML -
Declarative Large-Scale
Machine Learning on ApacheSpark
The Power of DataOps for Cloud and Digital Transformation
Companies have been trying to speed up their innovation delivery for many years but often at the cost of higher quality and stronger security. Despite billions invested to accelerate innovation, projects are too often slowed by data friction - the result of growing volumes of silo’d data and multiple requests for data.
Overcoming these sources of friction requires constant iteration across several key dimensions:
• Reducing the total cost of data by making it fast and efficient to deliver data, regardless of source or consumer. Automation and tooling is critical.
• Integrating security and governance into a seamless data delivery process. This requires integrated masking, but also a governance platform and process to ensure the right rules and access controls are in place.
• Breaking down silos between people and organizations. This starts with the organizational change to bring people together into one team, but requires technology change to provide self-service data access and control.
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"Developing Real-time Video Applications with CoaXPress," A Presentation from...
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Webinar: Three Reasons Why NAS is No Good for AI and Machine Learning
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“TinyML Isn’t Thinking Big Enough,” a Presentation from Perceive
“TinyML Isn’t Thinking Big Enough,” a Presentation from Perceive
Intro to DeepLearning4J on ApacheSpark SDS DL Workshop 16
Intro to DeepLearning4J on ApacheSpark SDS DL Workshop 16
WekaIO: Making Machine Learning Compute Bound Again
WekaIO: Making Machine Learning Compute Bound Again
Very large scale distributed deep learning on BigDL
Very large scale distributed deep learning on BigDL
Distributed Models Over Distributed Data with MLflow, Pyspark, and Pandas
Distributed Models Over Distributed Data with MLflow, Pyspark, and Pandas
Self driving computers active learning workflows with human interpretable ve...
Self driving computers active learning workflows with human interpretable ve...
The Pandemic Changes Everything, the Need for Speed and Resiliency
The Pandemic Changes Everything, the Need for Speed and Resiliency
Video Analytics on Hadoop webinar victor fang-201309
Video Analytics on Hadoop webinar victor fang-201309
Industrial production process visualization with the Elastic Stack in real-ti...
Industrial production process visualization with the Elastic Stack in real-ti...
IBM Middle East Data Science Connect 2016 - Doha, Qatar
IBM Middle East Data Science Connect 2016 - Doha, Qatar
Cloud Computing Was Built for Web Developers—What Does v2 Look Like for Deep...
Cloud Computing Was Built for Web Developers—What Does v2 Look Like for Deep...
Apache SystemML - Declarative Large-Scale Machine Learning
Apache SystemML - Declarative Large-Scale Machine Learning
The Power of DataOps for Cloud and Digital Transformation
The Power of DataOps for Cloud and Digital Transformation
Similar to "Implementing the TensorFlow Deep Learning Framework on Qualcomm’s Low-power DSP," a Presentation from Google
DevOpsDays 2018 - Migrating a Cloud Native App to k8s
Video record is on https://youtu.be/1qOuK4UwTvc
Jenkins Pipeline @ Scale. Building Automation Frameworks for Systems Integration
This is a follow-up presentation to my talk at CloudBees | Jenkins Automotive and Embedded Day 2016, where I was presenting Pipeline usage strategies for use-cases in the Embedded area. In this presentation I talk about Jenkins Pipeline features for automation frameworks and talk about lessons learned in several project.
How bigtop leveraged docker for build automation and one click hadoop provis...
What is Apache Bigtop?
Achieving Build Automation
One-Click Hadoop Provisioning
Bigtop at Trend Micro
AWS re:Invent 2016: Bringing Deep Learning to the Cloud with Amazon EC2 (CMP314)
Algorithmia is a startup with a mission to make state of the art machine learning discoverable by everyone&emdash;they offer the largest algorithm marketplace in the world, with over 2500 algorithms supporting tens of thousands of application developers. Algorithma is the first company to make deep learning, one of the most conceptually difficult areas of computing, accessible to any company via microservices. In this session, you learn how this startup has selected and optimized Amazon EC2 instances for various algorithms (including the latest generation of GPU optimized instances), to create a flexible and scalable platform. They also share their architecture and best practices for getting any computationally-intensive application started quickly.
Netflix Open Source: Building a Distributed and Automated Open Source Program
Netflix has been using and contributing to open source for several years. Over the years, Netflix has released over one hundred Netflix Open Source (aka NetflixOSS) libraries, servers, and technologies. Netflix engineers benefit by accepting contributions and gathering feedback with key collaborators around the world. Users of NetflixOSS from many industries benefit from our solutions including Big Data, Build and Delivery Tools, Runtime Services and Libraries, Data Persistence, Insight, Reliability and Performance, Security and User Interface. With such a large and mature open source program, Netflix has worked on approaches and tools that help manage and improve the NetflixOSS source offerings and communities. Netflix has taken a different approach to building support for open source as compared to other Internet scale companies. Come to this session to learn about the unique approaches Netflix has taken to both distribute and automate the responsibilities of building a world-class open source program.
Building a Distributed & Automated Open Source Program at Netflix
Andrew Spyker
Senior Software Engineer for Netflix
Find more by Andrew Spyker: http://www.slideshare.net/aspyker
All Things Open
October 26-27, 2016
Raleigh, North Carolina
Hadoop training in mumbai
hadoop training in mumbai at Asterix Solution is designed to scale up from single servers to thousands of machines, each offering local computation and storage. With the rate at which memory cost decreased the processing speed of data never increased and hence loading the large set of data is still a big headache and here comes Hadoop as the solution for it.
http://www.asterixsolution.com/big-data-hadoop-training-in-mumbai.html
LNUG: Having Your Node.js Cake and Eating It Too
Presentation given at LNUG Meeting on 27 September 2017
Machine Learning Standards
An overview of the different standards for ML model interchange. Based on the situation in April 2018.
Portfolio
I'm an enthusiastic, DevOps Evangelist with more than six years of experience, comprising of DevTools, Build & Release and Integration Administration.
Passionate about DevOps, Possess Rich experience in various DevTools used for Source Control, Build Automation, Continuous Integration, Continuous Deployment, Provisioning, Scripting and Monitoring.
Proven track record in Delivering best DevOps practice by automating the build, test and deployment in agile development life cycle, maximizing the velocity of development teams by automating release workflows (pipelines) from development.
Continuous Integration & Deployment of applications using Git, Maven, Nexus, Tuxedo, Docker, shell and Jenkins pipelines.
An individual contributor, well organized with strong problem solving and good communication skills and a team player.
Google does containers: Hello Kubernetes - Steve Wong and Vladimir Vivien - D...
Kubernetes is a production grade container orchestration system and one of the most popular open source projects released by Google. {code} by Dell EMC is focused on bringing a common storage interface. Kubernetes boasts a unique architecture and fresh perspective on running containerized applications used by companies like SoundCloud and The New York Times. Learn all about Kubernetes and see how {code} by Dell EMC is integrating REX-Ray to provide stateful application support to pods of containers.
Storage for containers and cloud-native deployments - Rancher Online Meetup -...
In our March 2017 Rancher Online Meetup we dove into running persistent storage for containers in Rancher. The presentation included an overview of persistent storage in Rancher, an introduction to Quobyte and an overview of Portworx.
Continuous delivery with jenkins pipelines (@WeAreDevelopers2017)
Continuous Delivery with Jenkins Pipelines
This lightening talk demonstrates how a continuous delivery deployment pipeline can be set up harnessing jenkins 2’s Pipeline as Code features as well as its brand new Blue Ocean User Experience.
Rootconf 2017 - State of the Open Source monitoring landscape
A talk about various Open Source tools for availability and functional monitoring, time series and metrics as well log and event management.
Webinar: End-to-End CI/CD with GitLab and DC/OS
Seven years ago, Apache Mesos was born as a platform to bring the distributed computing capabilities that powered the largest digital companies to the masses. Today, Mesosphere DC/OS technologies power more containers in production than any other software stack in the world, and has emerged as the premier platform for building and elastically scaling data-rich, modern applications and the associated CI/CD infrastructure across any infrastructure, public or private.
GitLab is an end-to-end software development and delivery platform with built-in CI/CD, monitoring, and performance metrics. With a unified experience for every step of the development lifecycle and seamless integration with container schedulers, GitLab provides the most efficient approach to reduce cycle time, increase velocity, and improve software quality.
In this webinar, you will learn how to combine DC/OS and GitLab to easily build a CI/CD infrastructure and build a complete CI/CD pipeline in minutes.
Slides cover:
1. An introduction to Apache Mesos and Mesosphere DC/OS and overview of DC/OS features and capabilities for developing, deploying, and operating containerized applications, microservices and CI/CD
2. An introduction to GitLab
3. How to use DC/OS and GitLab to build a CI/CD solution and go from idea to production
vBACD - Distributed Petabyte-Scale Cloud Storage with GlusterFS - 2/28
vBACD - Distributed Petabyte-Scale Cloud Storage with GlusterFS - 2/28CloudStack - Open Source Cloud Computing Project
GlusterFS is an open source scale-out NAS solution. The software is a powerful and flexible solution that simplifies the task of managing unstructured file data whether you have a few terabytes of storage or multiple petabytes. It’s no secret that unstructured data is growing like crazy, Gluster provides a solutions that scales capacity and performance as you need it and is an ideal fit for an IT environment that is increasingly virtualized and moving to the cloud.
There are two key ways that GlusterFS is beneficial for cloud builders:
1. Storage layer for VMs. If you're deploying Xen or KVM VMs on a private cloud, storing them on GlusterFS gives you the ability to migrate to different hypervisors, suspend and resume quickly - even on another hypervisor, scale out far beyond what other filesystems will allow, and utilize N-way replication for DR and HA
2. Unified storage layer for applications. With GlusterFS 3.3, you will be able to access your application data stores from an object (S3, Swift-style) interface, as well as a traditional POSIX-compatible NAS interface. This unified approach gives developers and admins the ability to access the same data store using a variety of different methods.
In this session, attendees will learn steps for deployment and some common use cases.
Speaker Bio
John Mark is an experienced veteran of all things open source and a self-described agitprop, agitator and advocate for those who volunteer countless, unpaid hours for a particular project or community. He first fell down the slippery slope of open source as a web developer at VA Linux Systems and eventually switched to the community team, beginning a career that has now lasted over ten years. Along the way, John Mark made stops at young, up-and-coming startups, such as Groundwork, Hyperic and then Gluster (later acquired by Red Hat). In between, there was a brief interlude at IDG World Expo, where he was the conference director for LinuxWorld, GridWorld and OSBC. His advice for companies who want to "do community" is to trust your community and give them the space to "just try s***." John Mark loves to perform community karaoke, and is available for weddings, funerals and Bar/Bat Mitzvahs
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DevOpsDays 2018 - Migrating a Cloud Native App to k8s
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Jenkins Pipeline @ Scale. Building Automation Frameworks for Systems Integration
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How bigtop leveraged docker for build automation and one click hadoop provis...
How bigtop leveraged docker for build automation and one click hadoop provis...
Google Tech Talk with Dr. Eric Brewer in Korea Apr.27.2015
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AWS re:Invent 2016: Bringing Deep Learning to the Cloud with Amazon EC2 (CMP314)
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Netflix Open Source: Building a Distributed and Automated Open Source Program
Netflix Open Source: Building a Distributed and Automated Open Source Program
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Google does containers: Hello Kubernetes - Steve Wong and Vladimir Vivien - D...
Google does containers: Hello Kubernetes - Steve Wong and Vladimir Vivien - D...
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Continuous delivery with jenkins pipelines (@WeAreDevelopers2017)
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Rootconf 2017 - State of the Open Source monitoring landscape
Rootconf 2017 - State of the Open Source monitoring landscape
vBACD - Distributed Petabyte-Scale Cloud Storage with GlusterFS - 2/28
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More from Edge AI and Vision Alliance
“How Arm’s Machine Learning Solution Enables Vision Transformers at the Edge,...
“How Arm’s Machine Learning Solution Enables Vision Transformers at the Edge,...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/how-arms-machine-learning-solution-enables-vision-transformers-at-the-edge-a-presentation-from-arm/
Stephen Su, Senior Segment Marketing Manager at Arm, presents the “How Arm’s Machine Learning Solution Enables Vision Transformers at the Edge” tutorial at the May 2024 Embedded Vision Summit.
AI at the edge has been transforming over the last few years, with newer use cases running more efficiently and securely. Most edge AI workloads were initially run on CPUs, but machine learning accelerators have gradually been integrated into SoCs, providing more efficient solutions. At the same time, ChatGPT has driven a sudden surge in interest in transformer-based models, which are primarily deployed using cloud resources. Soon, many transformer-based models will be modified to run effectively on edge devices.
In this presentation, Su explains the role of transformer-based models in vision applications and the challenges of implementing transformer models at the edge. Next, he introduces the latest Arm machine learning solution and how it enables the deployment of transformer-based vision networks at the edge. Finally, he shares an example implementation of a transformer-based embedded vision use case and uses this to contrast such solutions with those based on traditional CNN networks.“Nx EVOS: A New Enterprise Operating System for Video and Visual AI,” a Prese...
“Nx EVOS: A New Enterprise Operating System for Video and Visual AI,” a Prese...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/nx-evos-a-new-enterprise-operating-system-for-video-and-visual-ai-a-presentation-from-network-optix/
Nathan Wheeler, Co-founder and CEO of Network Optix, presents the “Nx EVOS: A New Enterprise Operating System for Video and Visual AI” tutorial at the May 2024 Embedded Vision Summit.
In most software domains, developers don’t write code at the bare-metal level; they build applications on top of operating systems, which provide commonly needed functionality. Yet, today, developers of video and AI applications are effectively writing their applications at the bare-metal level, building the “plumbing” themselves to handle basics like device discovery, storage management, security and model deployment. These developers need an operating system that supports their applications so they can focus on what really matters: the core functionality of their product.
Nx EVOS is the world’s first enterprise video operating system. EVOS revolutionizes video management, offering device discovery, bandwidth optimization and security features—in cloud and on device. Its support for AI pipelines and user management enables scalable deployment of AI applications across environments and platforms, and it’s trusted by leading organizations such as SpaceX. In this presentation, you’ll learn how Nx EVOS can save you time and effort in building your next vision product.“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation."OpenCV for High-performance, Low-power Vision Applications on Snapdragon," a...
"OpenCV for High-performance, Low-power Vision Applications on Snapdragon," a...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/opencv-for-high-performance-low-power-vision-applications-on-snapdragon-a-presentation-from-qualcomm/
Xin Zhong, Computer Vision Product Manager at Qualcomm Technologies, presents the “OpenCV for High-performance, Low-power Vision Applications on Snapdragon” tutorial at the May 2024 Embedded Vision Summit.
For decades, the OpenCV software library has been popular for developing computer vision applications. However, developers have found it challenging to create efficient implementations of their OpenCV applications on processors optimized for edge applications, like the Qualcomm Snapdragon family. As part of its comprehensive support for computer vision application developers, Qualcomm provides a variety of tools to enable developers to take full advantage of the heterogeneous computing resources in the Snapdragon processors.
In this talk, Zhong introduces a new element of Qualcomm’s computer vision tools suite: a version of OpenCV optimized for Snapdragon platforms, which allows developers to leverage and port their existing OpenCV-based applications seamlessly to Snapdragon platforms. Supporting OpenCV v4.x and later releases, this implementation contains unique Qualcomm-specific accelerations of OpenCV and OpenCV extension APIs. Zhong explains how this library enables developers to leverage existing OpenCV code to achieve superior performance and power savings on Snapdragon platforms.“Deploying Large Models on the Edge: Success Stories and Challenges,” a Prese...
“Deploying Large Models on the Edge: Success Stories and Challenges,” a Prese...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/deploying-large-models-on-the-edge-success-stories-and-challenges-a-presentation-from-qualcomm/
Vinesh Sukumar, Senior Director of Product Management at Qualcomm Technologies, presents the “Deploying Large Models on the Edge: Success Stories and Challenges” tutorial at the May 2024 Embedded Vision Summit.
In this talk, Dr. Sukumar explains and demonstrates how Qualcomm has been successful in deploying large generative AI and multimodal models on the edge for a variety of use cases in consumer and enterprise markets. He examines key challenges that must be overcome before large models at the edge can reach their full commercial potential. He also highlights how Qualcomm is addressing these challenges through upgraded processor hardware, improved developer tools and a comprehensive library of fully optimized AI models in the Qualcomm AI Hub.“Scaling Vision-based Edge AI Solutions: From Prototype to Global Deployment,...
“Scaling Vision-based Edge AI Solutions: From Prototype to Global Deployment,...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/scaling-vision-based-edge-ai-solutions-from-prototype-to-global-deployment-a-presentation-from-network-optix/
Maurits Kaptein, Chief Data Scientist at Network Optix and Professor at the University of Eindhoven, presents the “Scaling Vision-based Edge AI Solutions: From Prototype to Global Deployment” tutorial at the May 2024 Embedded Vision Summit.
The Embedded Vision Summit brings together innovators in silicon, devices, software and applications and empowers them to bring computer vision and perceptual AI into reliable and scalable products. However, integrating recent hardware, software and algorithm innovations into prime-time-ready products is quite challenging. Scaling from a proof of concept—for example, a novel neural network architecture performing a valuable task efficiently on a new piece of silicon—to an AI vision system installed in hundreds of sites requires surmounting myriad hurdles.
First, building on Network Optix’s 14 years of experience, Professor Kaptein details how to overcome the networking, fleet management, visualization and monetization challenges that come with scaling a global vision solution. Second, Kaptein discusses the complexities of making vision AI solutions device-agnostic and remotely manageable, proposing an open standard for AI model deployment to edge devices. The proposed standard aims to simplify market entry for silicon manufacturers and enhance scalability for solution developers. Kaptein outlines the standard’s core components and invites collaborative contributions to drive market expansion.“What’s Next in On-device Generative AI,” a Presentation from Qualcomm
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/whats-next-in-on-device-generative-ai-a-presentation-from-qualcomm/
Jilei Hou, Vice President of Engineering and Head of AI Research at Qualcomm Technologies, presents the “What’s Next in On-device Generative AI” tutorial at the May 2024 Embedded Vision Summit.
The generative AI era has begun! Large multimodal models are bringing the power of language understanding to machine perception, and transformer models are expanding to allow machines to understand using multiple types of sensors. This new wave of approaches is poised to revolutionize user experiences, disrupt industries and enable powerful new capabilities. For generative AI to reach its full potential, however, we must deploy it on edge devices, providing improved latency, pervasive interaction and enhanced privacy.
In this talk, Hou shares Qualcomm’s vision of the compelling opportunities enabled by efficient generative AI at the edge. He also identifies the key challenges that the industry must overcome to realize the massive potential of these technologies. And he highlights research and product development work that Qualcomm is doing to lead the way via an end-to-end system approach—including techniques for efficient on-device execution of LLMs, LVMs and LMMs, methods for orchestration of large models at the edge and approaches for adaptation and personalization.
“Learning Compact DNN Models for Embedded Vision,” a Presentation from the Un...
“Learning Compact DNN Models for Embedded Vision,” a Presentation from the Un...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/11/learning-compact-dnn-models-for-embedded-vision-a-presentation-from-the-university-of-maryland-at-college-park/
Shuvra Bhattacharyya, Professor at the University of Maryland at College Park, presents the “Learning Compact DNN Models for Embedded Vision” tutorial at the May 2023 Embedded Vision Summit.
In this talk, Bhattacharyya explores methods to transform large deep neural network (DNN) models into effective compact models. The transformation process that he focuses on—from large to compact DNN form—is referred to as pruning. Pruning involves the removal of neurons or parameters from a neural network. When performed strategically, pruning can lead to significant reductions in computational complexity without significant degradation in accuracy. It is sometimes even possible to increase accuracy through pruning.
Pruning provides a general approach for facilitating real-time inference in resource-constrained embedded computer vision systems. Bhattacharyya provides an overview of important aspects to consider when applying or developing a DNN pruning method and presents details on a recently introduced pruning method called NeuroGRS. NeuroGRS considers structures and trained weights jointly throughout the pruning process and can result in significantly more compact models compared to other pruning methods.“Introduction to Computer Vision with CNNs,” a Presentation from Mohammad Hag...
“Introduction to Computer Vision with CNNs,” a Presentation from Mohammad Hag...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/11/introduction-to-computer-vision-with-cnns-a-presentation-from-mohammad-haghighat/
Independent consultant Mohammad Haghighat presents the “Introduction to Computer Vision with Convolutional Neural Networks” tutorial at the May 2023 Embedded Vision Summit.
This presentation covers the basics of computer vision using convolutional neural networks. Haghighat begins by introducing some important conventional computer vision techniques and then transition to explaining the basics of machine learning and convolutional neural networks (CNNs) and showing how CNNs are used in visual perception.
Haghighat illustrates the building blocks and computational elements of neural networks through examples. This session provides an overview of how modern computer vision algorithms are designed, trained and used in real-world applications.“Selecting Tools for Developing, Monitoring and Maintaining ML Models,” a Pre...
“Selecting Tools for Developing, Monitoring and Maintaining ML Models,” a Pre...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/11/selecting-tools-for-developing-monitoring-and-maintaining-ml-models-a-presentation-from-yummly/
Parshad Patel, Data Scientist at Yummly, presents the “Selecting Tools for Developing, Monitoring and Maintaining ML Models” tutorial at the May 2023 Embedded Vision Summit.
With the boom in tools for developing, monitoring and maintaining ML models, data science teams have many options to choose from. Proprietary tools provided by cloud service providers are enticing, but teams may fear being locked in—and may worry that these tools are too costly or missing important features when compared with alternatives from specialized providers.
Fortunately, most proprietary, fee-based tools have an open-source component that can be integrated into a home-grown solution at low cost. This can be a good starting point, enabling teams to get started with modern tools without making big investments and leaving the door open to evolve tool selection over time. In this talk, Patel presents a step-by-step process for creating an MLOps tool set that enables you to deliver maximum value as a data scientist. He shares how Yummly built pipelines for model development and put them into production using open-source projects.“Building Accelerated GStreamer Applications for Video and Audio AI,” a Prese...
“Building Accelerated GStreamer Applications for Video and Audio AI,” a Prese...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/11/building-accelerated-gstreamer-applications-for-video-and-audio-ai-a-presentation-from-wave-spectrum/
Abdo Babukr, Accelerated Computing Consultant at Wave Spectrum, presents the “Building Accelerated GStreamer Applications for Video and Audio AI,” tutorial at the May 2023 Embedded Vision Summit.
GStreamer is a popular open-source framework for creating streaming media applications. Developers often use GStreamer to streamline the development of computer vision and audio perception applications. Since perceptual algorithms are often quite demanding in terms of processing performance, in many cases developers need to find ways to accelerate key GStreamer building blocks, taking advantage of specialized features of their target processor or co-processor.
In this talk, Babukr introduces GStreamer and shows how to use it to build computer vision and audio perception applications. He also shows how to create efficient, high-performance GStreamer applications that utilize specialized hardware features.“Understanding, Selecting and Optimizing Object Detectors for Edge Applicatio...
“Understanding, Selecting and Optimizing Object Detectors for Edge Applicatio...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/11/understanding-selecting-and-optimizing-object-detectors-for-edge-applications-a-presentation-from-walmart-global-tech/
Md Nasir Uddin Laskar, Staff Machine Learning Engineer at Walmart Global Tech, presents the “Understanding, Selecting and Optimizing Object Detectors for Edge Applications” tutorial at the May 2023 Embedded Vision Summit.
Object detectors count objects in a scene and determine their precise locations, while also labeling them. Object detection plays a crucial role in many vision applications, from autonomous driving to smart appliances. In many of these applications, it’s necessary or desirable to implement object detection at the edge.
In this presentation, Laskar explores the evolution of object detection algorithms, from traditional approaches to deep learning-based methods and transformer-based architectures. He delves into widely used approaches for object detection, such as two-stage R-CNNs and one-stage YOLO algorithms, and examines their strengths and weaknesses. And he provides guidance on how to evaluate and select an object detector for an edge application.“Introduction to Modern LiDAR for Machine Perception,” a Presentation from th...
“Introduction to Modern LiDAR for Machine Perception,” a Presentation from th...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/10/introduction-to-modern-lidar-for-machine-perception-a-presentation-from-the-university-of-ottawa/
Robert Laganière, Professor at the University of Ottawa and CEO of Sensor Cortek, presents the “Introduction to Modern LiDAR for Machine Perception” tutorial at the May 2023 Embedded Vision Summit.
In this presentation, Laganière provides an introduction to light detection and ranging (LiDAR) technology. He explains how LiDAR sensors work and their main advantages and disadvantages. He also introduces different approaches to LiDAR, including scanning and flash LiDAR.
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“Vision-language Representations for Robotics,” a Presentation from the Unive...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/11/vision-language-representations-for-robotics-a-presentation-from-the-university-of-pennsylvania/
Dinesh Jayaraman, Assistant Professor at the University of Pennsylvania, presents the “Vision-language Representations for Robotics” tutorial at the May 2023 Embedded Vision Summit.
In what format can an AI system best present what it “sees” in a visual scene to help robots accomplish tasks? This question has been a long-standing challenge for computer scientists and robotics engineers. In this presentation, Jayaraman provides insights into cutting-edge techniques being used to help robots better understand their surroundings, learn new skills with minimal guidance and become more capable of performing complex tasks.
Jayaraman discusses recent advances in unsupervised representation learning and explains how these approaches can be used to build visual representations that are appropriate for a controller that decides how the robot should act. In particular, he presents insights from his research group’s recent work on how to represent the constituent objects and entities in a visual scene, and how to combine vision and language in a way that permits effectively translating language-based task descriptions into images depicting the robot’s goals.“ADAS and AV Sensors: What’s Winning and Why?,” a Presentation from TechInsights
“ADAS and AV Sensors: What’s Winning and Why?,” a Presentation from TechInsightsEdge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/10/adas-and-av-sensors-whats-winning-and-why-a-presentation-from-techinsights/
Ian Riches, Vice President of the Global Automotive Practice at TechInsights, presents the “ADAS and AV Sensors: What’s Winning and Why?” tutorial at the May 2023 Embedded Vision Summit.
It’s clear that the number of sensors per vehicle—and the sophistication of these sensors—is growing rapidly, largely thanks to increased adoption of advanced safety and driver assistance features. In this presentation, Riches explores likely future demand for automotive radars, cameras and LiDARs.
Riches examines which vehicle features will drive demand out to 2030, how vehicle architecture change is impacting the market and what sorts of compute platforms these sensors will be connected to. Finally, he shares his firm’s vision of what the landscape could look like far beyond 2030, considering scenarios out to 2050 for automated driving and the resulting sensor demand.“Computer Vision in Sports: Scalable Solutions for Downmarkets,” a Presentati...
“Computer Vision in Sports: Scalable Solutions for Downmarkets,” a Presentati...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/10/computer-vision-in-sports-scalable-solutions-for-downmarkets-a-presentation-from-sportlogiq/
Mehrsan Javan, Co-founder and CTO of Sportlogiq, presents the “Computer Vision in Sports: Scalable Solutions for Downmarket Leagues” tutorial at the May 2023 Embedded Vision Summit.
Sports analytics is about observing, understanding and describing the game in an intelligent manner. In practice, this requires a fully automated, robust end-to-end pipeline, spanning from visual input, to player and group activities, to player and team evaluation to planning. Despite major advancements in computer vision and machine learning, today sports analytics solutions are limited to top leagues and are not widely available for downmarket leagues and youth sports.
In this talk, Javan explains how his company has developed scalable and robust computer vision solutions to democratize sport analytics and offer pro-league-level insights to leagues with modest resources, including youth leagues. He highlights key challenges—such as the requirement for low-cost, low-latency processing and the need for robustness despite variations in venues. He discusses the approaches Sportlogiq tried and how it ultimately overcame these challenges, including the use of transformers and fusion of multiple type of data streams to maximize accuracy.“Detecting Data Drift in Image Classification Neural Networks,” a Presentatio...
“Detecting Data Drift in Image Classification Neural Networks,” a Presentatio...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/10/detecting-data-drift-in-image-classification-neural-networks-a-presentation-from-southern-illinois-university/
Spyros Tragoudas, Professor and School Director at Southern Illinois University Carbondale, presents the “Detecting Data Drift in Image Classification Neural Networks” tutorial at the May 2023 Embedded Vision Summit.
An unforeseen change in the input data is called “drift,” and may impact the accuracy of machine learning models. In this talk, Tragoudas presents a novel scheme for diagnosing data drift in the input streams of image classification neural networks. His proposed method for drift detection and quantification uses a threshold dictionary for the prediction probabilities of each class in the neural network model.
The method is applicable to any drift type in images such as noise and weather effects, among others. Tragoudas shares experimental results on various data sets, drift types and neural network models to show that his proposed method estimates the drift magnitude with high accuracy, especially when the level of drift significantly impacts the model’s performance.“Deep Neural Network Training: Diagnosing Problems and Implementing Solutions...
“Deep Neural Network Training: Diagnosing Problems and Implementing Solutions...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/10/deep-neural-network-training-diagnosing-problems-and-implementing-solutions-a-presentation-from-sensor-cortek/
Fahed Hassanat, Chief Operating Officer and Head of Engineering at Sensor Cortek, presents the “Deep Neural Network Training: Diagnosing Problems and Implementing Solutions” tutorial at the May 2023 Embedded Vision Summit.
In this presentation, Hassanat delves into some of the most common problems that arise when training deep neural networks. He provides a brief overview of essential training metrics, including accuracy, precision, false positives, false negatives and F1 score.
Hassanat then explores training challenges that arise from problems with hyperparameters, inappropriately sized models, inadequate models, poor-quality datasets, imbalances within training datasets and mismatches between training and testing datasets. To help detect and diagnose training problems, he also covers techniques such as understanding performance curves, recognizing overfitting and underfitting, analyzing confusion matrices and identifying class interaction issues.
“AI Start-ups: The Perils of Fishing for Whales (War Stories from the Entrepr...
“AI Start-ups: The Perils of Fishing for Whales (War Stories from the Entrepr...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/10/ai-start-ups-the-perils-of-fishing-for-whales-war-stories-from-the-entrepreneurial-front-lines-a-presentation-from-seechange-technologies/
Tim Hartley, Vice President of Product for SeeChange Technologies, presents the “AI Start-ups: The Perils of Fishing for Whales (War Stories from the Entrepreneurial Front Lines)” tutorial at the May 2023 Embedded Vision Summit.
You have a killer idea that will change the world. You’ve thought through product-market fit and differentiation. You have seed funding and a world-beating team. Best of all, you’ve caught the attention of major players in your industry. You’ve reached peak “start-up”—that point of limitless possibility—when you go to bed with the same level of energy and enthusiasm you had when you woke. And then the first proof of concept starts…
In this talk, Hartley lays out some of the pitfalls that await those building the next big thing. Using real examples, he shares some of the dos and don’ts, particularly when dealing with that big potential first customer. Hartley discusses the importance of end-to-end design, ensuring your product solves real-world problems. He explores how far the big companies will tell you to jump—and then jump again—for free. And, most importantly, how to build long-term partnerships with major corporations without relying on over-promising sales pitches.“A Computer Vision System for Autonomous Satellite Maneuvering,” a Presentati...
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For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/10/a-computer-vision-system-for-autonomous-satellite-maneuvering-a-presentation-from-scout-space/
Andrew Harris, Spacecraft Systems Engineer at SCOUT Space, presents the “Developing a Computer Vision System for Autonomous Satellite Maneuvering” tutorial at the May 2023 Embedded Vision Summit.
Computer vision systems for mobile autonomous machines experience a wide variety of real-world conditions and inputs that can be challenging to capture accurately in training datasets. Few autonomous systems experience more challenging conditions than those in orbit. In this talk, Harris describes how SCOUT Space has designed and trained satellite vision systems using dynamic and physically informed synthetic image datasets.
Harris describes how his company generates synthetic data for this challenging environment and how it leverages new real-world data to improve our datasets. In particular, he explains how these synthetic datasets account for and can replicate real sources of noise and error in the orbital environment, and how his company supplements them with in-space data from the first SCOUT-Vision system, which has been in orbit since 2021.More from Edge AI and Vision Alliance (20)
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GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
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Bob Boule
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Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
UiPath Test Automation using UiPath Test Suite series, part 6
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
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Test Automation with generative AI and Open AI.
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A tale of scale & speed: How the US Navy is enabling software delivery from l...
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
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This paper presents Reef, a system for generating publicly verifiable succinct non-interactive zero-knowledge proofs that a committed document matches or does not match a regular expression. We describe applications such as proving the strength of passwords, the provenance of email despite redactions, the validity of oblivious DNS queries, and the existence of mutations in DNA. Reef supports the Perl Compatible Regular Expression syntax, including wildcards, alternation, ranges, capture groups, Kleene star, negations, and lookarounds. Reef introduces a new type of automata, Skipping Alternating Finite Automata (SAFA), that skips irrelevant parts of a document when producing proofs without undermining soundness, and instantiates SAFA with a lookup argument. Our experimental evaluation confirms that Reef can generate proofs for documents with 32M characters; the proofs are small and cheap to verify (under a second).
Paper: https://eprint.iacr.org/2023/1886
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Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Video Streaming: Then, Now, and in the Future
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
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"Implementing the TensorFlow Deep Learning Framework on Qualcomm’s Low-power DSP," a Presentation from Google
- 1. Copyright © 2017 Google 1 Implementing the TensorFlow Deep Learning Framework on Qualcomm’s Low-power DSP Pete Warden May 2017
- 2. Copyright © 2017 Google 2 • Google’s open source library for machine intelligence • tensorflow.org launched in Nov 2015 • Used by many production ML projects 2
- 3. Copyright © 2017 Google 3 TensorFlow and HVX
- 4. Copyright © 2017 Google 4 • Models run 8X faster, and use 1.4 watts versus ~5 watts on CPU TensorFlow supports Qualcomm’s Hexagon DSP Qualcomm Snapdragon 820 Processor featuring the Hexagon DSP DragonBoard 820c
- 5. Copyright © 2017 Google 5 • Started with my Embedded Vision Alliance talk last year • “Eight bits are enough” • Became clear from conversations with Qualcomm that there were possibilities with their existing hardware in the Snapdragon 820 How did this happen?
- 6. Copyright © 2017 Google 6 • Qualcomm implemented a quick sanity test using gemmlowp, our open source math library • That demonstrated 100 GOPs/second on realistic workloads using the HVX • More than 5x speed of CPU • Power usage expected to be much lower Next Steps
- 7. Copyright © 2017 Google 7 • Gemmlowp project has m, n, k values for InceptionV1 matrix multiplies Benchmark Details https://github.com/google/gemmlowp/blob/master/test/benchmark.cc#L283
- 8. Copyright © 2017 Google 8 • Gemmlowp results indicated around 200 GOPs/s (versus 25 GOPs/s on CPU) • End to end turned out to be around 90 ms, versus 700 ms on CPU • Was a good predictor of performance Results 8
- 9. Copyright © 2017 Google 9 TensorFlow code is at https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/hvx Qualcomm code is https://source.codeaurora.org/quic/hexagon_nn/nnlib
- 10. Copyright © 2017 Google 10 • Works by assembling a batch of ops on the CPU • Then sends them off to HVX via FastRPC • HVX runs it within its own code loop • Signals the AP when it's done
- 11. Copyright © 2017 Google 11 • TensorFlow handles splitting up the graph between HVX and CPU • Same mechanism is available for other accelerators too
- 12. Copyright © 2017 Google 12
- 13. Copyright © 2017 Google 13
- 14. Copyright © 2017 Google 14
- 15. Copyright © 2017 Google 15 Embedded TensorFlow
- 16. Copyright © 2017 Google 16 We work closely with chip builders
- 17. Copyright © 2017 Google 17 • Examples • ARM’s Compute Library • Movidius’s mvTensor tool • CEVA’s conversion tools • Intel’s contributions to https://github.com/google/gemmlowp • Qualcomm’s HVX collaboration We work closely with chip builders
- 18. Copyright © 2017 Google 18 Why?
- 19. Copyright © 2017 Google 19 • Mobile App Developers (including Snapchat) • Device builders • Home • Drones • Industrial • Medical • Automotive Lots of demand
- 20. Copyright © 2017 Google 20 • Full support for eight bit • Full stack: researchers, data centers, mobile apps, embedded devices • Main framework at Google • Shipping for vision on many apps, including PhotoScan and Snapchat What’s TensorFlow particularly good at?
- 21. Copyright © 2017 Google 21 • Support for eight-bit training • On-device training (already being used by Google Keyboard) • Better export pipeline (Graph Transform Tool) • Raspberry Pi • Jetson TX1 experimental support • Other chips? • Many more examples Embedded TensorFlow Roadmap
- 22. Copyright © 2017 Google 22 • ARM and Intel added code to https://github.com/google/gemmlowp • Worked with many others to support TensorFlow file format for conversion pipelines • We’re always open to conversations about our requirements and porting Collaborations with hardware vendors
- 23. Copyright © 2017 Google 23 • TensorFlow hands-on training class from the Embedded Vision Alliance, July 13 in Santa Clara • We’re always looking for chips, tools, systems companies to collaborate with • Please get in touch! • petewarden@google.com Future