1. The document summarizes several papers on deep learning and convolutional neural networks. It discusses techniques like pruning weights, trained quantization, Huffman coding, and designing networks with fewer parameters like SqueezeNet.
2. One paper proposes techniques to compress deep neural networks by pruning, trained quantization, and Huffman coding to reduce model size. It evaluates these techniques on networks for MNIST and ImageNet, achieving compression rates of 35x to 49x with no loss of accuracy.
3. Another paper introduces SqueezeNet, a CNN architecture with AlexNet-level accuracy but 50x fewer parameters and a model size of less than 0.5MB. It employs fire modules with 1x1 convolutions to
Improving Hardware Efficiency for DNN ApplicationsChester Chen
Speaker: Dr. Hai (Helen) Li is the Clare Boothe Luce Associate Professor of Electrical and Computer Engineering and Co-director of the Duke Center for Evolutionary Intelligence at Duke University
In this talk, I will introduce a few recent research spotlights by the Duke Center for Evolutionary Intelligence. The talk will start with the structured sparsity learning (SSL) method which attempts to learn a compact structure from a bigger DNN to reduce computation cost. It generates a regularized structure with high execution efficiency. Our experiments on CPU, GPU, and FPGA platforms show on average 3~5 times speedup of convolutional layer computation of AlexNet. Then, the implementation and acceleration of DNN applications on mobile computing systems will be introduced. MoDNN is a local distributed system which partitions DNN models onto several mobile devices to accelerate computations. ApesNet is an efficient pixel-wise segmentation network, which understands road scenes in real-time, and has achieved promising accuracy. Our prospects on the adoption of emerging technology will also be given at the end of this talk, offering the audiences an alternative thinking about the future evolution and revolution of modern computing systems.
This presentation focuses on Deep Learning (DL) concepts, such as neural networks, backprop, activation functions, and Convolutional Neural Networks. You'll also learn how to incorporate Deep Learning in Android applications. Basic knowledge of matrices is helpful for this session, which is targeted primarily to beginners.
A practical talk by Anirudh Koul aimed at how to run Deep Neural Networks to run on memory and energy constrained devices like smartphones. Highlights some frameworks and best practices.
Squeezing Deep Learning Into Mobile PhonesAnirudh Koul
A practical talk by Anirudh Koul aimed at how to run Deep Neural Networks to run on memory and energy constrained devices like smart phones. Highlights some frameworks and best practices.
Deep learning on mobile - 2019 Practitioner's GuideAnirudh Koul
The 2019 Guide to Deep Learning on Mobile, from Inference to Training on iOS and Android smartphones. Featuring CoreML, Tensorflow Lite, MLKit, Fritz, AutoML Approaches (Hardware Aware Neural Architecture Search) to make models more efficient, and lots of videos. Presented by Anirudh Koul, Siddha Ganju and Meher Anand Kasam. More details at PracticalDL.ai in the upcoming O'Reilly Book 'Practical Deep Learning on Cloud & Mobile'
Deep Learning Frameworks Using Spark on YARN by Vartika SinghData Con LA
Abstract:- Traditional machine learning and feature engineering algorithms are not efficient enough to extract complex and nonlinear patterns hallmarks of big data. Deep learning, on the other hand, helps translate the scale and complexity of the data into solutions like molecular interaction in drug design, the search for subatomic particles and automatic parsing of microscopic images. Co-locating a data processing pipeline with a deep learning framework makes data exploration/algorithm and model evolution much simpler, while streamlining data governance and lineage tracking into a more focused effort. In this talk, we will discuss and compare the different deep learning frameworks on Spark in a distributed mode, ease of integration with the Hadoop ecosystem, and relative comparisons in terms of feature parity.
Mastering Computer Vision Problems with State-of-the-art Deep LearningMiguel González-Fierro
Deep learning has been especially successful in computer vision tasks such as image classification because convolutional neural nets (CNNs) can create hierarchical
representations in an image. One of the most remarkable advances is ResNet, the CNN that surpassed human-level accuracy for the first time in history.
ImageNet competition has become the de facto benchmark for image classification in the research community. The “small” ImageNet data contains more than 1.2 million images distributed in 1,000 classes.
Miguel González-Fierro explains how to train a state of the art deep neural network, ResNet, using Microsoft RSever and MXNet with the ImageNet dataset. (While most of the deep learning libraries are programmed in C++ and Python, only MXNet offers an API for R programmers.) Miguel then demonstrates how to operationalize this training for real-world business problems related to image classification.
This talk was presented at Strata London 2017: https://conferences.oreilly.com/strata/strata-eu/public/schedule/detail/57428
Improving Hardware Efficiency for DNN ApplicationsChester Chen
Speaker: Dr. Hai (Helen) Li is the Clare Boothe Luce Associate Professor of Electrical and Computer Engineering and Co-director of the Duke Center for Evolutionary Intelligence at Duke University
In this talk, I will introduce a few recent research spotlights by the Duke Center for Evolutionary Intelligence. The talk will start with the structured sparsity learning (SSL) method which attempts to learn a compact structure from a bigger DNN to reduce computation cost. It generates a regularized structure with high execution efficiency. Our experiments on CPU, GPU, and FPGA platforms show on average 3~5 times speedup of convolutional layer computation of AlexNet. Then, the implementation and acceleration of DNN applications on mobile computing systems will be introduced. MoDNN is a local distributed system which partitions DNN models onto several mobile devices to accelerate computations. ApesNet is an efficient pixel-wise segmentation network, which understands road scenes in real-time, and has achieved promising accuracy. Our prospects on the adoption of emerging technology will also be given at the end of this talk, offering the audiences an alternative thinking about the future evolution and revolution of modern computing systems.
This presentation focuses on Deep Learning (DL) concepts, such as neural networks, backprop, activation functions, and Convolutional Neural Networks. You'll also learn how to incorporate Deep Learning in Android applications. Basic knowledge of matrices is helpful for this session, which is targeted primarily to beginners.
A practical talk by Anirudh Koul aimed at how to run Deep Neural Networks to run on memory and energy constrained devices like smartphones. Highlights some frameworks and best practices.
Squeezing Deep Learning Into Mobile PhonesAnirudh Koul
A practical talk by Anirudh Koul aimed at how to run Deep Neural Networks to run on memory and energy constrained devices like smart phones. Highlights some frameworks and best practices.
Deep learning on mobile - 2019 Practitioner's GuideAnirudh Koul
The 2019 Guide to Deep Learning on Mobile, from Inference to Training on iOS and Android smartphones. Featuring CoreML, Tensorflow Lite, MLKit, Fritz, AutoML Approaches (Hardware Aware Neural Architecture Search) to make models more efficient, and lots of videos. Presented by Anirudh Koul, Siddha Ganju and Meher Anand Kasam. More details at PracticalDL.ai in the upcoming O'Reilly Book 'Practical Deep Learning on Cloud & Mobile'
Deep Learning Frameworks Using Spark on YARN by Vartika SinghData Con LA
Abstract:- Traditional machine learning and feature engineering algorithms are not efficient enough to extract complex and nonlinear patterns hallmarks of big data. Deep learning, on the other hand, helps translate the scale and complexity of the data into solutions like molecular interaction in drug design, the search for subatomic particles and automatic parsing of microscopic images. Co-locating a data processing pipeline with a deep learning framework makes data exploration/algorithm and model evolution much simpler, while streamlining data governance and lineage tracking into a more focused effort. In this talk, we will discuss and compare the different deep learning frameworks on Spark in a distributed mode, ease of integration with the Hadoop ecosystem, and relative comparisons in terms of feature parity.
Mastering Computer Vision Problems with State-of-the-art Deep LearningMiguel González-Fierro
Deep learning has been especially successful in computer vision tasks such as image classification because convolutional neural nets (CNNs) can create hierarchical
representations in an image. One of the most remarkable advances is ResNet, the CNN that surpassed human-level accuracy for the first time in history.
ImageNet competition has become the de facto benchmark for image classification in the research community. The “small” ImageNet data contains more than 1.2 million images distributed in 1,000 classes.
Miguel González-Fierro explains how to train a state of the art deep neural network, ResNet, using Microsoft RSever and MXNet with the ImageNet dataset. (While most of the deep learning libraries are programmed in C++ and Python, only MXNet offers an API for R programmers.) Miguel then demonstrates how to operationalize this training for real-world business problems related to image classification.
This talk was presented at Strata London 2017: https://conferences.oreilly.com/strata/strata-eu/public/schedule/detail/57428
On-device machine learning: TensorFlow on AndroidYufeng Guo
Machine learning has traditionally been the solely performed on servers and high performance machines. But there is great value is having on-device machine learning for mobile devices. Doing ML inference on mobile devices has huge potential and is still in its early stages. However, it's already more powerful than most realize.
In this demo-oriented talk, you will see some examples of deep learning models used for local prediction on mobile devices. Learn how to use TensorFlow to implement a machine learning model that is tailored to a custom dataset, and start making delightful experiences today!
Faster deep learning solutions from training to inference - Michele Tameni - ...Codemotion
Intel Deep Learning SDK enables using of optimized open source deep-learning frameworks, including Caffe and TensorFlow through a step-by-step wizard or iPython interactive notebooks. It includes easy and fast installation of all depended libraries and advanced tools for easy data pre-processing and model training, optimization and deployment, providing an end-to-end solution to the problem. In addition, it supports scale-out on multiple computers for training, as well as using compression methods for deployment of the models on various platforms, addressing memory and speed constraints.
For real world application, convolutional neural network(CNN) model can take more than 100MB of space and can be computationally too expensive. Therefore, there are multiple methods to reduce this complexity in the state of art. Ristretto is a plug-in to Caffe framework that employs several model approximation methods. For this projects, first a CNN model is trained for Cifar-10 dataset with Caffe, then Ristretto will be use to generate multiple approximated version of the trained model using different schemes. The goal of this projects is comparison of the models in terms of execution performance, model size and cache utilizations in the test or inference phase. The same steps are done with Tensorflow and Quantisation tool. The quantisation schemes of Tensorflow and Ristretto are then compared.
A simplified way of approaching machine learning and deep learning from the ground up. The case for deep learning and an attempt to develop intuition for how/why it works. Advantages, state-of-the-art, and trends.
Presented at NYU Center for Genomics for NY Deep Learning Meetup
Yinyin Liu presents at SD Robotics Meetup on November 8th, 2016. Deep learning has made great success in image understanding, speech, text recognition and natural language processing. Deep Learning also has tremendous potential to tackle the challenges in robotic vision, and sensorimotor learning in a robotic learning environment. In this talk, we will talk about how current and future deep learning technologies can be applied for robotic applications.
This is a 2 hours overview on the deep learning status as for Q1 2017.
Starting with some basic concepts, continue to basic networks topologies , tools, HW/Accelerators and finally Intel's take on the the different fronts.
Image Classification Done Simply using Keras and TensorFlow Rajiv Shah
This presentation walks through the process of building an image classifier using Keras with a TensorFlow backend. It will give a basic understanding of image classification and show the techniques used in industry to build image classifiers. The presentation will start with building a simple convolutional network, augmenting the data, using a pretrained network, and finally using transfer learning by modifying the last few layers of a pretrained network. The classification will be based on the classic example of classifying cats and dogs. The code for the presentation can be found at https://github.com/rajshah4/image_keras, and the presentation will discuss how to extend the code to your own pictures to make a custom image classifier.
For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/auvizsystems/embedded-vision-training/videos/pages/may-2015-embedded-vision-summit
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Nagesh Gupta, CEO and Founder of Auviz Systems, presents the "Trade-offs in Implementing Deep Neural Networks on FPGAs" tutorial at the May 2015 Embedded Vision Summit.
Video and images are a key part of Internet traffic—think of all the data generated by social networking sites such as Facebook and Instagram—and this trend continues to grow. Extracting usable information from video and images is thus a growing requirement in the data center. For example, object and face recognition are valuable for a wide range of uses, from social applications to security applications. Deep neural networks are currently the most popular form of convolutional neural networks (CNN) used in data centers for such applications. 3D convolutions are a core part of CNNs. Nagesh presents alternative implementations of 3D convolutions on FPGAs, and discusses trade-offs among them.
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/wavecomp/embedded-vision-training/videos/pages/may-2017-embedded-vision-summit-nicol
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Chris Nicol, CTO at Wave Computing, presents the "New Dataflow Architecture for Machine Learning" tutorial at the May 2017 Embedded Vision Summit.
Data scientists have made tremendous advances in the use of deep neural networks (DNNs) to enhance business models and service offerings. But training DNNs can take a week or more using traditional hardware solutions that rely on legacy architectures that are limited in performance and scalability. New innovations that can reduce training time for both image-centric and text-centric deep neural networks will lead to an explosion of new applications. Dr. Chris Nicol, Wave Computing’s Chief Technology Officer, examines the performance challenge faced by data scientists today. Nicol outlines the technical factors underlying this bottleneck for systems relying on CPUs, GPUs, FPGAs and ASICs, and introduces a new dataflow-centric approach to DNN training.
On-device machine learning: TensorFlow on AndroidYufeng Guo
Machine learning has traditionally been the solely performed on servers and high performance machines. But there is great value is having on-device machine learning for mobile devices. Doing ML inference on mobile devices has huge potential and is still in its early stages. However, it's already more powerful than most realize.
In this demo-oriented talk, you will see some examples of deep learning models used for local prediction on mobile devices. Learn how to use TensorFlow to implement a machine learning model that is tailored to a custom dataset, and start making delightful experiences today!
Faster deep learning solutions from training to inference - Michele Tameni - ...Codemotion
Intel Deep Learning SDK enables using of optimized open source deep-learning frameworks, including Caffe and TensorFlow through a step-by-step wizard or iPython interactive notebooks. It includes easy and fast installation of all depended libraries and advanced tools for easy data pre-processing and model training, optimization and deployment, providing an end-to-end solution to the problem. In addition, it supports scale-out on multiple computers for training, as well as using compression methods for deployment of the models on various platforms, addressing memory and speed constraints.
For real world application, convolutional neural network(CNN) model can take more than 100MB of space and can be computationally too expensive. Therefore, there are multiple methods to reduce this complexity in the state of art. Ristretto is a plug-in to Caffe framework that employs several model approximation methods. For this projects, first a CNN model is trained for Cifar-10 dataset with Caffe, then Ristretto will be use to generate multiple approximated version of the trained model using different schemes. The goal of this projects is comparison of the models in terms of execution performance, model size and cache utilizations in the test or inference phase. The same steps are done with Tensorflow and Quantisation tool. The quantisation schemes of Tensorflow and Ristretto are then compared.
A simplified way of approaching machine learning and deep learning from the ground up. The case for deep learning and an attempt to develop intuition for how/why it works. Advantages, state-of-the-art, and trends.
Presented at NYU Center for Genomics for NY Deep Learning Meetup
Yinyin Liu presents at SD Robotics Meetup on November 8th, 2016. Deep learning has made great success in image understanding, speech, text recognition and natural language processing. Deep Learning also has tremendous potential to tackle the challenges in robotic vision, and sensorimotor learning in a robotic learning environment. In this talk, we will talk about how current and future deep learning technologies can be applied for robotic applications.
This is a 2 hours overview on the deep learning status as for Q1 2017.
Starting with some basic concepts, continue to basic networks topologies , tools, HW/Accelerators and finally Intel's take on the the different fronts.
Image Classification Done Simply using Keras and TensorFlow Rajiv Shah
This presentation walks through the process of building an image classifier using Keras with a TensorFlow backend. It will give a basic understanding of image classification and show the techniques used in industry to build image classifiers. The presentation will start with building a simple convolutional network, augmenting the data, using a pretrained network, and finally using transfer learning by modifying the last few layers of a pretrained network. The classification will be based on the classic example of classifying cats and dogs. The code for the presentation can be found at https://github.com/rajshah4/image_keras, and the presentation will discuss how to extend the code to your own pictures to make a custom image classifier.
For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/auvizsystems/embedded-vision-training/videos/pages/may-2015-embedded-vision-summit
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Nagesh Gupta, CEO and Founder of Auviz Systems, presents the "Trade-offs in Implementing Deep Neural Networks on FPGAs" tutorial at the May 2015 Embedded Vision Summit.
Video and images are a key part of Internet traffic—think of all the data generated by social networking sites such as Facebook and Instagram—and this trend continues to grow. Extracting usable information from video and images is thus a growing requirement in the data center. For example, object and face recognition are valuable for a wide range of uses, from social applications to security applications. Deep neural networks are currently the most popular form of convolutional neural networks (CNN) used in data centers for such applications. 3D convolutions are a core part of CNNs. Nagesh presents alternative implementations of 3D convolutions on FPGAs, and discusses trade-offs among them.
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/wavecomp/embedded-vision-training/videos/pages/may-2017-embedded-vision-summit-nicol
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Chris Nicol, CTO at Wave Computing, presents the "New Dataflow Architecture for Machine Learning" tutorial at the May 2017 Embedded Vision Summit.
Data scientists have made tremendous advances in the use of deep neural networks (DNNs) to enhance business models and service offerings. But training DNNs can take a week or more using traditional hardware solutions that rely on legacy architectures that are limited in performance and scalability. New innovations that can reduce training time for both image-centric and text-centric deep neural networks will lead to an explosion of new applications. Dr. Chris Nicol, Wave Computing’s Chief Technology Officer, examines the performance challenge faced by data scientists today. Nicol outlines the technical factors underlying this bottleneck for systems relying on CPUs, GPUs, FPGAs and ASICs, and introduces a new dataflow-centric approach to DNN training.
For the full video of this presentation, please visit:
http://www.embedded-vision.com/industry-analysis/video-interviews-demos/overcoming-barriers-consumer-adoption-vision-enabled-produc
For more information about embedded vision, please visit:
http://www.embedded-vision.com
John Feland, CEO and Founder of Argus Insights, presents the "Overcoming Barriers to Consumer Adoption of Vision-enabled Products and Services" tutorial at the May 2015 Embedded Vision Summit.
Visual intelligence is being deployed in a growing range of consumer products, including smartphones, tablets, security cameras, laptops (especially with Intel’s RealSense push), and even smartwatches. The demos are always cool. But does vision work for regular consumers? Do consumers see vision as a value add or just another feature to be ignored?
In this talk, John investigates the best and worst of consumer product embedded vision implementations as told by real consumers, based on Argus Insights’ extensive portfolio of consumer data. John examines where current products fall short of consumers’ needs. And, he illuminates successful implementations to show how their vision capabilities create value in the lives of consumers. Case studies will include examples from Dropcam, Intel RealSense, HTC’s M8, and vision-enabled drones such as the DJI Phantom 2 Vision+.
Unsupervised Classification of Images: A ReviewCSCJournals
Unsupervised image classification is the process by which each image in a dataset is identified to be a member of one of the inherent categories present in the image collection without the use of labelled training samples. Unsupervised categorisation of images relies on unsupervised machine learning algorithms for its implementation. This paper identifies clustering algorithms and dimension reduction algorithms as the two main classes of unsupervised machine learning algorithms needed in unsupervised image categorisation, and then reviews how these algorithms are used in some notable implementation of unsupervised image classification algorithms.
For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/dec-2015-member-meeting-nauto
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Stefan Heck of Nauto delivers the presentation, "A Vision of Safety," at the December 2015 Embedded Vision Alliance Member Meeting. Heck explain how his innovative start-up is using embedded vision to bring improved safety to existing vehicles, reducing insurance costs in the process.
For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/may-2014-embedded-vision-summit-khronos
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Neil Trevett, President of Khronos and Vice President at NVIDIA, presents the "OpenVX Hardware Acceleration API for Embedded Vision Applications and Libraries" tutorial at the May 2014 Embedded Vision Summit.
This presentation introduces OpenVX, a new application programming interface (API) from the Khronos Group. OpenVX enables performance and power optimized vision algorithms for use cases such as face, body and gesture tracking, smart video surveillance, automatic driver assistance systems, object and scene reconstruction, augmented reality, visual inspection, robotics and more.
OpenVX enables significant implementation innovation while maintaining a consistent API for developers. OpenVX can be used directly by applications or to accelerate higher-level middleware with platform portability. OpenVX complements the popular OpenCV open source vision library that is often used for application prototyping.
For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/altera/embedded-vision-training/videos/pages/may-2015-embedded-vision-summit
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Deshanand Singh, Director of Software Engineering at Altera, presents the "Efficient Implementation of Convolutional Neural Networks using OpenCL on FPGAs" tutorial at the May 2015 Embedded Vision Summit.
Convolutional neural networks (CNN) are becoming increasingly popular in embedded applications such as vision processing and automotive driver assistance systems. The structure of CNN systems is characterized by cascades of FIR filters and transcendental functions. FPGA technology offers a very efficient way of implementing these structures by allowing designers to build custom hardware datapaths that implement the CNN structure. One challenge of using FPGAs revolves around the design flow that has been traditionally centered around tedious hardware description languages.
In this talk, Deshanand gives a detailed explanation of how CNN algorithms can be expressed in OpenCL and compiled directly to FPGA hardware. He gives detail on code optimizations and provides comparisons with the efficiency of hand-coded implementations.
For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/bdti/embedded-vision-training/videos/pages/may-2014-embedded-vision-summit-techni-0
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Jeff Bier, President and co-founder of BDTI and founder of the Embedded Vision Alliance, presents the "Trends and Recent Developments in Processors for Vision" tutorial at the May 2014 Embedded Vision Summit.
Processor suppliers are investing intensively in new processors for vision applications, employing a diverse range of architecture approaches to meet the conflicting requirements of high performance, low cost, energy efficiency, and ease of application development.
In this presentation, Bier draws from BDTI's ongoing processor evaluation work to highlight significant recent developments in processors for vision applications, including mobile application processors, graphics processing units, and specialized vision processors. He also explores what BDTI considers to be the most significant trends in processors for vision—such as the increasing use of heterogeneous architectures—and the implications of these trends for system designers and application developers.
【論文紹介】Fashion Style in 128 Floats: Joint Ranking and Classification using Wea...Hirokatsu Kataoka
CVPR2016にてシモセラ・エドガー氏が発表した、StyleNetの紹介資料です。
"Fashion Style in 128 Floats: Joint Ranking and Classification using Weak Data for Feature Extraction," Edgar Simo-Serra and Hiroshi Ishikawa, in CVPR2016.
論文情報
http://hi.cs.waseda.ac.jp/~esimo/publications/SimoSerraCVPR2016.pdf
プロジェクトページ
http://hi.cs.waseda.ac.jp/~esimo/ja/research/stylenet/
For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/ceva/embedded-vision-training/videos/pages/may-2016-embedded-vision-summit-siegel
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Yair Siegel, Director of Segment Marketing at CEVA, presents the "Fast Deployment of Low-power Deep Learning on CEVA Vision Processors" tutorial at the May 2016 Embedded Vision Summit.
Image recognition capabilities enabled by deep learning are benefitting more and more applications, including automotive safety, surveillance and drones. This is driving a shift towards running neural networks inside embedded devices. But, there are numerous challenges in squeezing deep learning into resource-limited devices. This presentation details a fast path for taking a neural network from research into an embedded implementation on a CEVA vision processor core, making use of CEVA’s neural network software framework. Siegel explains how the CEVA framework integrates with existing deep learning development environments like Caffe, and how it can be used to create low-power embedded systems with neural network capabilities.
For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/arm/embedded-vision-training/videos/pages/may-2016-embedded-vision-summit-iodice
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Gian Marco Iodice, Software Engineer at ARM, presents the "Using SGEMM and FFTs to Accelerate Deep Learning" tutorial at the May 2016 Embedded Vision Summit.
Matrix Multiplication and the Fast Fourier Transform are numerical foundation stones for a wide range of scientific algorithms. With the emergence of deep learning, they are becoming even more important, particularly as use cases extend into mobile and embedded devices. In this presentation, lodice discusses and analyzes how these two key, computationally-intensive algorithms can be used to gain significant performance improvements for convolutional neural network (CNN) implementations.
After a brief introduction to the nature of CNN computations, Iodice explores the use of GEMM (General Matrix Multiplication) and mixed-radix FFTs to accelerate 3D convolution. He shows examples of OpenCL implementations of these functions and highlights their advantages, limitations and trade-offs. Central to the techniques explored is an emphasis on cache-efficient memory accesses and the crucial role of reduced-precision data types.
For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/dec-2016-member-meeting-uofw
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Professor Jeff Bilmes of the University of Washington delivers the presentation "Image and Video Summarization" at the December 2016 Embedded Vision Alliance Member Meeting. Bilmes provides an overview of the state of the art in image and video summarization.
U-Net is a convolutional neural network (CNN) architecture designed for semantic segmentation tasks, especially in the field of medical image analysis. It was introduced by Olaf Ronneberger, Philipp Fischer, and Thomas Brox in 2015. The name "U-Net" comes from its U-shaped architecture.
Key features of the U-Net architecture:
U-Shaped Design: U-Net consists of a contracting path (downsampling) and an expansive path (upsampling). The architecture resembles the letter "U" when visualized.
Contracting Path (Encoder):
The contracting path involves a series of convolutional and pooling layers.
Each convolutional layer is followed by a rectified linear unit (ReLU) activation function and possibly other normalization or activation functions.
Pooling layers (usually max pooling) reduce spatial dimensions, capturing high-level features.
Expansive Path (Decoder):
The expansive path involves a series of upsampling and convolutional layers.
Upsampling is achieved using transposed convolution (also known as deconvolution or convolutional transpose).
Skip connections are established between corresponding layers in the contracting and expansive paths. These connections help retain fine-grained spatial information during the upsampling process.
Skip Connections:
Skip connections concatenate feature maps from the contracting path to the corresponding layers in the expansive path.
These connections facilitate the fusion of low-level and high-level features, aiding in precise localization.
Final Layer:
The final layer typically uses a convolutional layer with a softmax activation function for multi-class segmentation tasks, providing probability scores for each class.
U-Net's architecture and skip connections help address the challenge of segmenting objects with varying sizes and shapes, which is often encountered in medical image analysis. Its success in this domain has led to its application in other areas of computer vision as well.
The U-Net architecture has also been extended and modified in various ways, leading to improvements like the U-Net++ architecture and variations with attention mechanisms, which further enhance the segmentation performance.
U-Net's intuitive design and effectiveness in semantic segmentation tasks have made it a cornerstone in the field of medical image analysis and an influential architecture for researchers working on segmentation challenges.
RunPool: A Dynamic Pooling Layer for Convolution Neural NetworkPutra Wanda
Deep learning (DL) has achieved a significant performance in computer vision problems, mainly in automatic feature extraction and representation. However, it is not easy to determine the best pooling method in a different case study. For instance, experts can implement the best types of pooling in image processing cases, which might not be optimal for various tasks. Thus, it is
required to keep in line with the philosophy of DL. In dynamic neural network architecture, it is not practically possible to find
a proper pooling technique for the layers. It is the primary reason why various pooling cannot be applied in the dynamic and multidimensional dataset. To deal with the limitations, it needs to construct an optimal pooling method as a better option than max pooling and average pooling. Therefore, we introduce a dynamic pooling layer called RunPool to train the convolutional
neuralnetwork(CNN)architecture.RunPoolpoolingisproposedtoregularizetheneuralnetworkthatreplacesthedeterministic
pooling functions. In the final section, we test the proposed pooling layer to address classification problems with online social network (OSN) dataset
LOAD BALANCED CLUSTERING WITH MIMO UPLOADING TECHNIQUE FOR MOBILE DATA GATHER...Munisekhar Gunapati
A three-layer framework is proposed for mobile data collection in wireless sensor networks, which includes the sensor layer, cluster head layer, and mobile collector (called SenCar) layer. The framework employs distributed load balanced clustering and dual data uploading, which is referred to as LBC-MIMO. The objective is to achieve good scalability, long network lifetime and low data collection latency. At the sensor layer, a distributed load balanced clustering (LBC) algorithm is proposed for sensors to self-organize themselves into clusters. In contrast to existing clustering methods, our scheme generates multiple cluster heads in each cluster to balance the work load and facilitate dual data uploading. At the cluster head layer, the inter-cluster transmission range is carefully chosen to guarantee the connectivity among the clusters. Multiple cluster heads within a cluster cooperate with each other to perform energy-saving inter-cluster communications. Through inter-cluster transmissions, cluster head information is forwarded to SenCar for its moving trajectory planning. At the mobile collector layer, SenCar is equipped with two antennas, which enables two cluster heads to simultaneously upload data to SenCar in each time by utilizing multi-user multiple-input and multiple-output (MU-MIMO) technique. The trajectory planning for SenCar is optimized to fully utilize dual data uploading capability by properly selecting polling points in each cluster. By visiting each selected polling point, SenCar can efficiently gather data from cluster heads and transport the data to the static data sink. Extensive simulations are conducted to evaluate the effectiveness of the proposed LBC-MIMO scheme. The results show that when each cluster has at most two cluster heads, LBC-MIMO achieves over 50 percent energy saving per node and 60 percent energy saving on cluster heads comparing with data collection through multi-hop relay to the static data sink, and 20 percent shorter data collection time compared to traditional mobile data gathering.
Recurrent Neural Networks (RNNs) represent the reference class of Deep Learning models for learning from sequential data. Despite the widespread success, a major downside of RNNs and commonly derived ‘gating’ variants (LSTM, GRU) is given by the high cost of the involved training algorithms. In this context, an increasingly popular alternative is the Reservoir Computing (RC) approach, which enables limiting the training algorithm to operate only on a restricted set of (output) parameters. RC is appealing for several reasons, including the amenability of being implemented in low-powerful edge devices, enabling adaptation and personalization in IoT and cyber-physical systems applications.
This webinar will introduce Reservoir Computing from scratch, covering all the fundamental design topics as well as good practices. It is targeted to both researchers and practitioners that are interested in setting up fastly-trained Deep Learning models for sequential data.
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
Towards Dropout Training for Convolutional Neural Networks Mah Sa
Design inspired by : https://www.slideshare.net/roelofp/python-for-image-understanding-deep-learning-with-convolutional-neural-nets?qid=06301e83-f65e-40a9-92a2-201664cd6119&v=&b=&from_search=1
Special tank to him....
Development of 3D convolutional neural network to recognize human activities ...journalBEEI
Human activity recognition (HAR) is recently used in numerous applications including smart homes to monitor human behavior, automate homes according to human activities, entertainment, falling detection, violence detection, and people care. Vision-based recognition is the most powerful method widely used in HAR systems implementation due to its characteristics in recognizing complex human activities. This paper addresses the design of a 3D convolutional neural network (3D-CNN) model that can be used in smart homes to identify several numbers of activities. The model is trained using KTH dataset that contains activities like (walking, running, jogging, handwaving handclapping, boxing). Despite the challenges of this method due to the effectiveness of the lamination, background variation, and human body variety, the proposed model reached an accuracy of 93.33%. The model was implemented, trained and tested using moderate computation machine and the results show that the proposal was successfully capable to recognize human activities with reasonable computations.
발표자: 배재성(KAIST 석사과정)
발표일: 2018.10.
최근 딥러닝을 이용한 방법은 다양한 음성 인식 과제에서 괄목할 만한 성과를 내고 있습니다. 특히 Convolutional Neural Network (CNN)을 이용한 방식은 지역적인 특징 (local feature)들을 효과적으로 잡아낼 수 있기 때문에 비교적 짧은 시간 의존도를 가지는 음성 키워드 인식이나 음소 단위 인식과 같은 과제들에서 활발히 사용되고 있습니다. 그러나 CNN은 낮은 레벨의 특징들 간의 공간적 관계성을 고려하지 않는다는 한계점이 있습니다. 이를 극복하기 위해 캡슐 네트워크 구조를 도입하여 음성 스펙트로그램에서 추출된 특징들의 공간적 관계성을 고려하고자 하였습니다. 구글 음성 단어 데이터셋에서 CNN과 그 성능을 비교해 보았으며, 깨끗한 환경과 잡음 환경 모두에서 주목할만한 성능 향상을 이끌어 냈습니다.
The presentation is coverong the convolution neural network (CNN) design.
First,
the main building blocks of CNNs will be introduced. Then we systematically
investigate the impact of a range of recent advances in CNN architectures and
learning methods on the object categorization (ILSVRC) problem. In the
evaluation, the influence of the following choices of the architecture are
tested: non-linearity (ReLU, ELU, maxout, compatibility with batch
normalization), pooling variants (stochastic, max, average, mixed), network
width, classifier design (convolution, fully-connected, SPP), image
pre-processing, and of learning parameters: learning rate, batch size,
cleanliness of the data, etc.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
3. 3
Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman
Coding
Song Han, Huizi Mao, William J. Dally
International Conference on Learning Representations ICLR2016
http://arXiv.org/abs/1510.00149
Learning both Weights and Connections for Efficient Neural Networks
Song Han, Jeff Pool, John Tran, William J. Dally
Neural Information Processing Systems NIPS2015
http://arxiv.org/abs/1506.02626
EIE: Efficient Inference Engine on Compressed Deep Neural Network
Song Han, Xingyu Liu, Huizi Mao, Jing Pu, Ardavan Pedram, Mark A. Horowitz, William J. Dally
International Symposium on Computer Architecture ISCA2016
http://arXiv.org/abs/1602.01528
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
Forrest N. Iandola, Song Han, Matthew W. Moskewicz, Khalid Ashraf, William J. Dally, Kurt Keutzer
Technical Report 2016
http://arXiv.org/abs/1602.07360
Recent developments in Deep Learning
4. 4
LeNet. The first successful applications of Convolutional Networks were developed by Yann LeCun
in 1990’s. Of these, the best known is the LeNet architecture that was used to read zip codes,
digits, etc.
AlexNet. The first work that popularized Convolutional Networks in Computer Vision was
the AlexNet, developed by Alex Krizhevsky, Ilya Sutskever and Geoff Hinton. The AlexNet was
submitted to the ImageNet ILSVRC challenge in 2012 and significantly outperformed the second
runner-up (top 5 error of 16% compared to runner-up with 26% error). The Network had a very
similar architecture to LeNet, but was deeper, bigger, and featured Convolutional Layers stacked on
top of each other (previously it was common to only have a single CONV layer always immediately
followed by a POOL layer).
VGGNet. The runner-up in ILSVRC 2014 was the network from Karen Simonyan and Andrew
Zisserman that became known as the VGGNet. Its main contribution was in showing that the depth
of the network is a critical component for good performance. Their final best network contains 16
CONV/FC layers and, appealingly, features an extremely homogeneous architecture that only
performs 3x3 convolutions and 2x2 pooling from the beginning to the end. Their pretrained model is
available for plug and play use in Caffe. A downside of the VGGNet is that it is more expensive to
evaluate and uses a lot more memory and parameters (140M). Most of these parameters are in the
first fully connected layer, and it was since found that these FC layers can be removed with no
performance downgrade, significantly reducing the number of necessary parameters.
Convolutional Neural Networks (CNNs / ConvNets)
http://cs231n.github.io/convolutional-networks/
Recent developments in Deep Learning
6. 6
Deep Learning – Paper 1
1 INTRODUCTION
2 NETWORK PRUNING
3 TRAINED QUANTIZATION AND WEIGHT SHARING
3.1 WEIGHT SHARING
3.2 INITIALIZATION OF SHARED WEIGHTS
3.3 FEED-FORWARD AND BACK-PROPAGATION
4 HUFFMAN CODING
5 EXPERIMENTS
5.1 LENET-300-100 AND LENET-5 ON MNIST
5.2 ALEXNET ON IMAGENET
5.3 VGG-16 ON IMAGENET
6 DISCUSSIONS
6.1 PRUNING AND QUANTIZATION WORKING TOGETHER
6.2 CENTROID INITIALIZATION
6.3 SPEEDUP AND ENERGY EFFICIENCY
6.4 RATIO OF WEIGHTS, INDEX AND CODEBOOK
7 RELATED WORK
8 FUTURE WORK
9 CONCLUSION
10. 10
Deep Learning – Paper 1
THE MNIST DATABASE of handwritten digits
http://yann.lecun.com/exdb/mnist/
Visual Geometry Group (University of Oxford)
http://www.robots.ox.ac.uk/~vgg/research/very_deep/
Alex Krizhevsky https://www.cs.toronto.edu/~kriz/
The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes,
with 6000 images per class. There are 50000 training images and 10000 test images
20. 20
Deep Learning – Paper 2
NIPS2015 Review
http://media.nips.cc/nipsbooks/nipspapers/paper_files/nips28/reviews/708.html
21. 21
Deep Learning – Paper 2
[7] Mark Horowitz. Energy table for 45nm process, Stanford VLSI wiki
Mark Horowitz Professor of Electrical Engineering and Computer Science
VLSI, Hardware, Graphics and Imaging, Applying Engineering to Biology
43. 43
Deep Learning – Paper 4
1. Introduction and Motivation
More efficient distributed training
Less overhead when exporting new models to clients
Feasible FPGA and embedded deployment
2. Related Work
2.1. Model Compression
2.2. CNN Microarchitecture
2.3. CNN Macroarchitecture
2.4. Neural Network Design Space Exploration
3. SqueezeNet: preserving accuracy with few parameters
3.1. Architectural Design Strategies
Strategy 1. Replace 3x3 filters with 1x1 filters
Strategy 2. Decrease the number of input channels to 3x3 filters
Strategy 3. Downsample late in the network so that convolution layers have large activation maps
3.2. The Fire Module
3.3. The SqueezeNet architecture
3.3.1 Other SqueezeNet details
5. CNN Microarchitecture Design Space Exploration
5.1. CNN Microarchitecture metaparameters
5.2. Squeeze Ratio
5.3. Trading off 1x1 and 3x3 filters
6. CNN Macroarchitecture Design Space Exploration
7. Model Compression Design Space Exploration
7.1. Sensitivity Analysis: Where to Prune or Add parameters
Sensitivity analysis applied to model compression
Sensitivity analysis applied to increasing accuracy
7.2. Improving Accuracy by Densifying Sparse Models
8. Conclusions
Rectified linear units improve restricted boltzmann machines.
V. Nair and G. E. Hinton. In ICML, 2010. 3