International Conference on AI and Mobile Services
Services Conference Federation (SCF)
San Diego, CA, USA
June 2019
Artificial Intelligence on the edge is a matter of great importance towards the enhancement of smart devices that rely on operations with real-time constraints. Despite the rapid growth of computational power in embedded systems, such as smartphones, wearable devices, drones and FPGAs, the deployment of highly complex and considerably big models remains challenging. Optimized execution requires managing memory allocation efficiently, to avoid overloading, and exploiting the available hardware resources for acceleration, which is not trivial given the non standardized access to such resources. We present PolimiDL, an open source framework for the acceleration of Deep Learning inference on mobile and embedded systems with limited resources and heterogeneous architectures. Experimental results show competitive results w.r.t. TensorFlow Lite for the execution of small models.
Deep Learning for Computer Vision: A comparision between Convolutional Neural...Vincenzo Lomonaco
This document describes a study comparing Convolutional Neural Networks (CNNs) and Hierarchical Temporal Memories (HTMs) on object recognition tasks. The study implements a CNN using Theano, creates a new benchmark of image sequences from the NORB dataset, and evaluates the performance of CNNs and HTMs on the original NORB dataset and new image sequences. The results show that while CNNs achieve higher accuracy on the original NORB data, HTMs are more competitive on the image sequences and can achieve comparable performance using less training data. The study proves that bio-inspired approaches like HTM can advance deep learning research.
This document discusses networks and deep learning, with a focus on their application to analyzing the COVID-19 pandemic. It begins with an overview of networks and graph theory concepts. It then discusses how deep learning, specifically graph neural networks, can be used to analyze networks and learn representations of nodes. Applications discussed include traffic prediction and modeling disease spread. It also introduces the SIR model for modeling epidemics and the basic reproduction number metric.
Robust Ensemble Classifier Combination Based on Noise Removal with One-Class SVMFerhat Ozgur Catak
The document describes a proposed approach for robust ensemble classifier combination based on noise removal with one-class SVM. The approach partitions an input dataset into sub-datasets, applies noise removal to each sub-dataset using one-class SVM, creates local classifier ensembles for each sub-dataset, and combines the ensemble classifiers using weighted voting. It aims to improve classification accuracy by reducing noise and training ensemble classifiers on partitions of the data. The document outlines the basic idea, discusses preliminaries like one-class SVM and AdaBoost, and describes experiments to evaluate the proposed approach.
A system was developed able to retrieve specific documents from a document collection. In this system the query is given in text by the user and then transformed into image. Appropriate features were in order to capture the general shape of the query, and ignore details due to noise or different fonts. In order to demonstrate the effectiveness of our system, we used a collection of noisy documents and we compared our results with those of a commercial OCR package.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2021/09/explainability-in-computer-vision-a-machine-learning-engineers-overview-a-presentation-from-altaml/
Navaneeth Kamballur Kottayil, Lead Machine Learning Developer at AltaML, presents the “Explainability in Computer Vision: A Machine Learning Engineer’s Overview” tutorial at the May 2021 Embedded Vision Summit.
With the increasing use of deep neural networks in computer vision applications, it has become more difficult for developers to explain how their algorithms work. This can make it difficult to establish trust and confidence among customers and other stakeholders, such as regulators. Lack of explainability also makes it more difficult for developers to improve their solutions.
In this talk, Kottayil introduces methods for enabling explainability in deep-learning-based computer vision solutions. He also illustrates some of these techniques via real-world examples, and shows how they can be used to improve customer trust in computer vision models, to debug computer vision models, to obtain additional insights about data and to detect bias in models.
Framework for Contextual Outlier Identification using Multivariate Analysis a...IJECEIAES
Majority of the existing commercial application for video surveillance system only captures the event frames where the accuracy level of captures is too poor. We reviewed the existing system to find that at present there is no such research technique that offers contextual-based scene identification of outliers. Therefore, we presented a framework that uses unsupervised learning approach to perform precise identification of outliers for a given video frames concerning the contextual information of the scene. The proposed system uses matrix decomposition method using multivariate analysis to maintain an equilibrium better faster response time and higher accuracy of the abnormal event/object detection as an outlier. Using an analytical methodology, the proposed system blocking operation followed by sparsity to perform detection. The study outcome shows that proposed system offers an increasing level of accuracy in contrast to the existing system with faster response time.
TRANSFER LEARNING BASED IMAGE VISUALIZATION USING CNNijaia
Image classification is a popular machine learning based applications of deep learning. Deep learning techniques are very popular because they can be effectively used in performing operations on image data in large-scale. In this paper CNN model was designed to better classify images. We make use of feature extraction part of inception v3 model for feature vector calculation and retrained the classification layer with these feature vector. By using the transfer learning mechanism the classification layer of the CNN model was trained with 20 classes of Caltech101 image dataset and 17 classes of Oxford 17 flower image dataset. After training, network was evaluated with testing dataset images from Oxford 17 flower dataset and Caltech101 image dataset. The mean testing precision of the neural network architecture with Caltech101 dataset was 98 % and with Oxford 17 Flower image dataset was 92.27 %.
Deep Convolutional Neural Network based Intrusion Detection SystemSri Ram
In the present era, the cyberspace is growing tremendously and Intrusion detection system (IDS) plays an key role in it to ensure the information security. The IDS, which works in network and host level, should be capable of identifying various malicious attacks. The job of network based IDS is to differentiate between normal and malicious traffic data and raise alert in case of an attack. Apart from the traditional signature and anomaly based approaches, many researchers have employed various Deep Learning (DL) techniques for detecting intrusion as DL models are capable of extracting salient features automatically from the input data. The application of Deep Convolutional Neural Network (DCNN), which is utilized quite often for solving research problems in image processing and vision fields, is not explored much for IDS. In this paper, a DCNN architecture for IDS which is trained on KDDCUP 99 data set is proposed. This work also shows that the DCNN-IDS model performs superior when compared with other existing works.
International Journal of Computational Engineering Research(IJCER)ijceronline
The document discusses image compression using artificial neural networks. It begins with an introduction to image compression and the need for it. Then it reviews various existing neural network approaches for image compression, including backpropagation networks, hierarchical networks, multilayer feedforward networks, and radial basis function networks. It proposes a new approach using a multilayer perceptron with a modified Levenberg-Marquardt training algorithm to improve compression performance. Authentication and protection would be incorporated by exploiting the one-to-one mapping and one-way properties of neural networks. The proposed system is described as compressing images using neural networks trained with a modified LM algorithm to achieve high compression ratios while maintaining image quality.
Deep Learning: Chapter 11 Practical MethodologyJason Tsai
Lecture for Deep Learning 101 study group to be held on June 9th, 2017.
Reference book: https://www.deeplearningbook.org/
Past video archives: https://goo.gl/hxermB
Initiated by Taiwan AI Group (https://www.facebook.com/groups/Taiwan.AI.Group/)
Comparing Incremental Learning Strategies for Convolutional Neural NetworksVincenzo Lomonaco
In the last decade, Convolutional Neural Networks (CNNs) have shown to perform incredibly well in many computer vision tasks such as object recognition and object detection, being able to extract meaningful high-level invariant features. However, partly because of their complex training and tricky hyper-parameters tuning, CNNs have been scarcely studied in the context of incremental learning where data are available in consecutive batches and retraining the model from scratch is unfeasible. In this work we compare different incremental learning strategies for CNN based architectures, targeting real-word applications.
If you are interested in this work please cite:
Lomonaco, V., & Maltoni, D. (2016, September). Comparing Incremental Learning Strategies for Convolutional Neural Networks. In IAPR Workshop on Artificial Neural Networks in Pattern Recognition (pp. 175-184). Springer International Publishing.
For further information visit my website: http://www.vincenzolomonaco.com/
In this deck from the GPU Technology Conference, Thorsten Kurth from Lawrence Berkeley National Laboratory and Josh Romero from NVIDIA present: Exascale Deep Learning for Climate Analytics.
"We'll discuss how we scaled the training of a single deep learning model to 27,360 V100 GPUs (4,560 nodes) on the OLCF Summit HPC System using the high-productivity TensorFlow framework. We discuss how the neural network was tweaked to achieve good performance on the NVIDIA Volta GPUs with Tensor Cores and what further optimizations were necessary to provide excellent scalability, including data input pipeline and communication optimizations, as well as gradient boosting for SGD-type solvers. Scalable deep learning becomes more and more important as datasets and deep learning models grow and become more complicated. This talk is targeted at deep learning practitioners who are interested in learning what optimizations are necessary for training their models efficiently at massive scale."
Watch the video: https://wp.me/p3RLHQ-kgT
Learn more: https://ml4sci.lbl.gov/home
and
https://www.nvidia.com/en-us/gtc/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
This document proposes EfficientDet, a new family of object detectors that achieve better accuracy and efficiency across a wide range of resource constraints. The key contributions are:
1. A weighted bi-directional feature pyramid network (BiFPN) that introduces learnable weights to efficiently fuse multi-scale features from different levels.
2. A compound scaling method that jointly scales the resolution, depth, and width of the backbone, feature network, and box/class prediction networks for higher accuracy.
3. Combining EfficientNet backbones with BiFPN and compound scaling, EfficientDet achieves state-of-the-art 52.2% AP on COCO while being 4x smaller and using 13x
One-shot learning is an object categorization problem in computer vision. Whereas most machine learning based object categorization algorithms require training on hundreds or thousands of images and very large datasets, one-shot learning aims to learn information about object categories from one, or only a few, training images
PR-258: From ImageNet to Image Classification: Contextualizing Progress on Be...Jinwon Lee
TensorFlow Korea 논문읽기모임 PR12 258번째 논문 review입니다.
이번 논문은 MIT에서 나온 From ImageNet to Image Classification: Contextualizing Progress on Benchmarks입니다.
Deep Learning 하시는 분들이면 ImageNet 모르시는 분들이 없을텐데요, 이 논문은 ImageNet의 labeling 방법의 한계와 문제점에 대해서 얘기하고 top-1 accuracy 기반의 평가 방법에도 문제가 있을 수 있음을 지적하고 있습니다.
ImageNet data의 20% 이상이 multi object를 포함하고 있지만 그 중에 하나만 정답으로 인정되는 문제가 있고, annotation 방법의 한계로 인하여 실제로 사람이 생각하는 것과 다른 class가 정답으로 labeling되어 있는 경우도 많았습니다. 또한 terrier만 20종이 넘는 등 전문가가 아니면 판단하기 어려운 label도 많다는 문제도 있었구요. 이 밖에도 다양한 실험을 통해서 정량적인 분석과 함께 human-in-the-loop을 이용한 평가로 현재 model들의 성능이 어디까지 와있는지, 그리고 앞으로 더 높은 성능을 내기 위해서 data labeling 측면에서 해결해야할 과제는 무엇인지에 대해서 이야기하고 있습니다. 논문이 양이 좀 많긴 하지만 기술적인 내용이 별로 없어서 쉽게 읽으실 수 있는데요, 자세한 내용이 궁금하신 분들은 영상을 참고해주세요!
논문링크: https://arxiv.org/abs/2005.11295
발표영상링크: https://youtu.be/CPMgX5ikL_8
Scene classification using Convolutional Neural Networks - Jayani WithanawasamWithTheBest
The document discusses scene classification using convolutional neural networks (CNNs). It begins with an outline of the topic, then provides background on computer vision as an AI problem and the importance and challenges of scene classification. It introduces CNNs as a deep learning technique for visual pattern recognition, describing their hierarchical organization and components like convolution and pooling layers. The document also discusses traditional machine learning approaches versus deep learning for scene classification and frameworks like Caffe that can be used to implement CNNs.
FPGA Hardware Accelerator for Machine Learning
Machine learning publications and models are growing exponentially, outpacing Moore's law. Hardware acceleration using FPGAs, GPUs, and ASICs can provide performance gains over CPU-only implementations for machine learning workloads. FPGAs allow for reprogramming after manufacturing and can accelerate parts of machine learning algorithms through customized hardware while sharing computations between the FPGA and CPU. Vitis AI is a software stack that optimizes machine learning models for deployment on Xilinx FPGAs, providing pre-optimized models, tools for optimization and quantization, and high-level APIs.
MemGuard: Memory Bandwidth Reservation System for Efficient Performance Isola...Heechul Yun
This document describes MemGuard, an operating system mechanism for providing efficient per-core memory performance isolation on commercial off-the-shelf hardware. MemGuard uses memory bandwidth reservation to guarantee each core's minimum memory bandwidth. It then performs predictive bandwidth donation and on-demand reclaiming to redistribute excess bandwidth, improving overall utilization. Evaluation shows MemGuard isolates performance and eliminates over 50% slowdown of a foreground real-time task due to interference, while maximizing throughput via bandwidth sharing.
This document discusses low cost supercomputing using Linux clusters. It begins with an introduction to parallel processing and clustering. Clusters offer a way to use multiple computers together as a single system for higher performance and lower costs. The document then covers parallel processing schemes and provides a conceptual overview of clusters. It discusses cluster design considerations including topology, hardware specifications, and software requirements. Linux is identified as a suitable operating system for clustering. The document outlines features and benefits of clustering, such as data sharing and parallel processing. It provides examples of clustering applications in fields like web serving, simulation, and science.
There are many challenges on FPGA design such as: FPGA Selection, System Design Challenges, Power and Resource optimization, Verification of Design etc.
Each and every FPGA Engineer face this challenges, so if they prepare for such challenges then they can accomplish and optimize FPGA based project or design in time and within budget.
For more details and consultation: www.digitronixnepal.com, email: digitronixnepali@gmail.com
Project Vault is a secure computing environment developed by Google's ATAP group. It uses a microSD card to provide an encrypted environment that works with any operating system. The project is open source and uses an FPGA-based hardware security module for encryption and decryption. It also uses a custom real-time operating system called microSEL and an OpenRISC 1200 processor. Project Vault aims to provide a portable secure computing solution.
Evaluating UCIe based multi-die SoC to meet timing and power Deepak Shankar
This document discusses evaluating a UCIe-based multi-die system-on-chip (SoC) using system modeling to meet timing and power constraints. It provides an overview of UCIe and how it can be used to connect multiple dies. It then describes assembling a system model in VisualSim Architect using UCIe components to analyze configurations and optimize latency, bandwidth, and power. Examples of multi-media and automotive applications using UCIe-based chiplet designs are also presented.
This document contains a summary of Ameya Kasbekar's experience and qualifications. He currently works as a Senior Software Engineer at Qualcomm Technologies Inc, where he has designed and developed modem algorithms and supported commercialization of new chipsets over 9 years. Previously, he worked as a Software Engineer at Infosys Technologies Ltd and in system administration at Clemson University. He has a Master's degree in Computer Science from Clemson University and a Bachelor's degree in Computer Engineering.
From Rack scale computers to Warehouse scale computersRyousei Takano
This document discusses the transition from rack-scale computers to warehouse-scale computers through the disaggregation of technologies. It provides examples of rack-scale architectures like Open Compute Project and Intel Rack Scale Architecture. For warehouse-scale computers, it examines HP's The Machine project using application-specific cores, universal memory, and photonics fabric. It also outlines UC Berkeley's FireBox project utilizing 1 terabit/sec optical fibers, many-core systems-on-chip, and non-volatile memory modules connected via high-radix photonic switches.
(Im2col)accelerating deep neural networks on low power heterogeneous architec...Bomm Kim
This document discusses accelerating deep neural networks on low power heterogeneous architectures. Specifically, it focuses on accelerating the inference time of the VGG-16 neural network on the ODROID-XU4 board, which contains an ARM CPU and Mali GPU. The authors develop parallel versions of VGG-16 using OpenMP for the CPU and OpenCL for the GPU. Several optimizations are explored in OpenCL, including work groups, vector data types, and the CLBlast library. The best OpenCL implementation achieves a 9.4x speedup over the original serial version.
Trends in Systems and How to Get Efficient Performanceinside-BigData.com
In this video from Switzerland HPC Conference, Martin Hilgeman from Dell presents: HPC Workload Efficiency and the Challenges for System Builders.
"With all the advances in massively parallel and multi-core computing with CPUs and accelerators it is often overlooked whether the computational work is being done in an efficient manner. This efficiency is largely being determined at the application level and therefore puts the responsibility of sustaining a certain performance trajectory into the hands of the user. It is observed that the adoption rate of new hardware capabilities is decreasing and lead to a feeling of diminishing returns. This presentation shows the well-known laws of parallel performance from the perspective of a system builder. It also covers through the use of real case studies, examples of how to program for energy efficient parallel application performance."
Watch the video: http://wp.me/p3RLHQ-gIS
Learn more: http://dell.com
and
http://www.hpcadvisorycouncil.com/events/2017/swiss-workshop/agenda.php
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
The document discusses grid computing and provides examples. It begins with an introduction to supercomputers and provides Param Padma as an example. It then defines grid computing, discussing its evolution and advantages over supercomputers. Design considerations for grid computing include assigning work randomly to nodes to check for accurate results due to lack of central control. Implementation involves using middleware like BOINC and Alchemi, which are described. The document outlines service-oriented grid architecture and challenges. It provides examples of grid initiatives worldwide like TeraGrid in the US and Garuda in India.
FPGA Conference 2021: Breaking the TOPS ceiling with sparse neural networks -...Numenta
Nick Ni (Xilinx) and Lawrence Spracklen (Numenta) presented a talk at the FGPA Conference Europe on July 8th, 2021. In this talk, they presented a neuroscience approach to optimize state-of-the-art deep learning networks into sparse topology and how it can unlock significant performance gains on FPGAs without major loss of accuracy. They then walked through the FPGA implementation where they exploited the advantage of sparse networks with a unique Domain Specific Architecture (DSA).
System on Chip is a an IC that integrates all the components of an electronic system. This presentation is based on the current trends and challenges in the IP based SOC design.
The document discusses various code optimization techniques for embedded C programming, including:
1) Floating-point to fixed-point conversion to reduce cycle count and energy consumption.
2) Array folding and loop tiling/blocking to improve memory usage and locality of references.
3) Loop splitting to improve efficiency by handling regular and exception cases separately.
4) Simple loop transformations like unrolling to reduce overhead and improve speed.
Dynamic memory allocation is discouraged in safety-critical embedded systems like avionics in favor of more predictable allocators like stack-based, thread-local, and in-memory databases to increase performance, stability, and predictability.
Performance of State-of-the-Art Cryptography on ARM-based MicroprocessorsHannes Tschofenig
Position paper for the NIST Lightweight Cryptography Workshop, 20th and 21st July 2015, Gaithersburg, US.
The link to the workshop is available at: http://www.nist.gov/itl/csd/ct/lwc_workshop2015.cfm
Exploring emerging technologies in the HPC co-design spacejsvetter
This document discusses emerging technologies for high performance computing (HPC), focusing on heterogeneous computing and non-volatile memory. It provides an overview of HPC architectures past and present, highlighting the trend toward more heterogeneous systems using GPUs and other accelerators. The document discusses challenges for applications to adapt to these changing architectures. It also explores potential future technologies like 3D memory and discusses the Department of Energy's efforts in codesign centers to facilitate collaboration between application developers and emerging hardware.
Similar to Accelerating Deep Learning Inference on Mobile Systems (20)
Development of Chatbot Using AI/ML Technologiesmaisnampibarel
The rapid advancements in artificial intelligence and natural language processing have significantly transformed human-computer interactions. This thesis presents the design, development, and evaluation of an intelligent chatbot capable of engaging in natural and meaningful conversations with users. The chatbot leverages state-of-the-art deep learning techniques, including transformer-based architectures, to understand and generate human-like responses.
Key contributions of this research include the implementation of a context- aware conversational model that can maintain coherent dialogue over extended interactions. The chatbot's performance is evaluated through both automated metrics and user studies, demonstrating its effectiveness in various applications such as customer service, mental health support, and educational assistance. Additionally, ethical considerations and potential biases in chatbot responses are examined to ensure the responsible deployment of this technology.
The findings of this thesis highlight the potential of intelligent chatbots to enhance user experience and provide valuable insights for future developments in conversational AI.
Understanding Cybersecurity Breaches: Causes, Consequences, and PreventionBert Blevins
Cybersecurity breaches are a growing threat in today’s interconnected digital landscape, affecting individuals, businesses, and governments alike. These breaches compromise sensitive information and erode trust in online services and systems. Understanding the causes, consequences, and prevention strategies of cybersecurity breaches is crucial to protect against these pervasive risks.
Cybersecurity breaches refer to unauthorized access, manipulation, or destruction of digital information or systems. They can occur through various means such as malware, phishing attacks, insider threats, and vulnerabilities in software or hardware. Once a breach happens, cybercriminals can exploit the compromised data for financial gain, espionage, or sabotage. Causes of breaches include software and hardware vulnerabilities, phishing attacks, insider threats, weak passwords, and a lack of security awareness.
The consequences of cybersecurity breaches are severe. Financial loss is a significant impact, as organizations face theft of funds, legal fees, and repair costs. Breaches also damage reputations, leading to a loss of trust among customers, partners, and stakeholders. Regulatory penalties are another consequence, with hefty fines imposed for non-compliance with data protection regulations. Intellectual property theft undermines innovation and competitiveness, while disruptions of critical services like healthcare and utilities impact public safety and well-being.
Social media management system project report.pdfKamal Acharya
The project "Social Media Platform in Object-Oriented Modeling" aims to design
and model a robust and scalable social media platform using object-oriented
modeling principles. In the age of digital communication, social media platforms
have become indispensable for connecting people, sharing content, and fostering
online communities. However, their complex nature requires meticulous planning
and organization.This project addresses the challenge of creating a feature-rich and
user-friendly social media platform by applying key object-oriented modeling
concepts. It entails the identification and definition of essential objects such as
"User," "Post," "Comment," and "Notification," each encapsulating specific
attributes and behaviors. Relationships between these objects, such as friendships,
content interactions, and notifications, are meticulously established.The project
emphasizes encapsulation to maintain data integrity, inheritance for shared behaviors
among objects, and polymorphism for flexible content handling. Use case diagrams
depict user interactions, while sequence diagrams showcase the flow of interactions
during critical scenarios. Class diagrams provide an overarching view of the system's
architecture, including classes, attributes, and methods .By undertaking this project,
we aim to create a modular, maintainable, and user-centric social media platform that
adheres to best practices in object-oriented modeling. Such a platform will offer users
a seamless and secure online social experience while facilitating future enhancements
and adaptability to changing user needs.
How to Manage Internal Notes in Odoo 17 POSCeline George
In this slide, we'll explore how to leverage internal notes within Odoo 17 POS to enhance communication and streamline operations. Internal notes provide a platform for staff to exchange crucial information regarding orders, customers, or specific tasks, all while remaining invisible to the customer. This fosters improved collaboration and ensures everyone on the team is on the same page.
Unblocking The Main Thread - Solving ANRs and Frozen FramesSinan KOZAK
In the realm of Android development, the main thread is our stage, but too often, it becomes a battleground where performance issues arise, leading to ANRS, frozen frames, and sluggish Uls. As we strive for excellence in user experience, understanding and optimizing the main thread becomes essential to prevent these common perforrmance bottlenecks. We have strategies and best practices for keeping the main thread uncluttered. We'll examine the root causes of performance issues and techniques for monitoring and improving main thread health as wel as app performance. In this talk, participants will walk away with practical knowledge on enhancing app performance by mastering the main thread. We'll share proven approaches to eliminate real-life ANRS and frozen frames to build apps that deliver butter smooth experience.
FD FAN.pdf forced draft fan for boiler operation and run its very important f...MDHabiburRhaman1
FD fan or forced draft fan, draws air from the atmosphere and forces it into the furnace through a preheater. These fans are located at the inlet of the boiler to push high pressure fresh air into combustion chamber, where it mixes with the fuel to produce positive pressure. and A forced draft fan (FD fan) is a fan that is used to push air into a boiler or other combustion chamber. It is located at the inlet of the boiler and creates a positive pressure in the combustion chamber, which helps to ensure that the fuel burns properly.
The working principle of a forced draft fan is based on the Bernoulli principle, which states that the pressure of a fluid decreases as its velocity increases. The fan blades rotate and impart momentum to the air, which causes the air to accelerate. This acceleration of the air creates a lower pressure at the outlet of the fan, which draws air in from the inlet.
The amount of air that is pushed into the boiler by the FD fan is determined by the fan’s capacity and the pressure differential between the inlet and outlet of the fan. The fan’s capacity is the amount of air that it can move per unit of time, and the pressure differential is the difference in pressure between the inlet and outlet of the fan.
The FD fan is an essential component of any boiler system. It helps to ensure that the fuel burns properly and that the boiler operates efficiently.
Here are some of the benefits of using a forced draft fan:Improved combustion efficiency: The FD fan helps to ensure that the fuel burns completely, which results in improved combustion efficiency.
Reduced emissions: The FD fan helps to reduce emissions by ensuring that the fuel burns completely.
Increased boiler capacity: The FD fan can increase the capacity of the boiler by providing more air for combustion.
Improved safety: The FD fan helps to improve safety by preventing the buildup of flammable gases in the boiler.
Forced Draft Fan ( Full form of FD Fan) is a type of fan supplying pressurized air to a system. In the case of a Steam Boiler Assembly, this FD fan is of great importance. The Forced Draft Fan (FD Fan) plays a crucial role in supplying the necessary combustion air to the steam boiler assembly, ensuring efficient and optimal combustion processes. Its pressurized airflow promotes the complete and controlled burning of fuel, enhancing the overall performance of the system.What is the FD fan in a boiler?
In a boiler system, the FD fan, or Forced Draft Fan, plays a crucial role in ensuring efficient combustion and proper air circulation within the boiler. Its primary function is to supply the combustion air needed for the combustion process.
The FD fan works by drawing in ambient air and then forcing it into the combustion chamber, creating the necessary air-fuel mixture for the combustion process. This controlled air supply ensures that the fuel burns efficiently, leading to optimal heat transfer and energy production.
In summary, the FD fan i
Best Practices for Password Rotation and Tools to Streamline the ProcessBert Blevins
Securing sensitive data is crucial for both individuals and enterprises in the digital era. Password rotation, or regularly changing passwords, has long been a standard security practice. Despite some debate over its effectiveness, password rotation remains an important part of comprehensive security strategies. This guide will explore best practices for password rotation and highlight tools to streamline the process.
The history of rotating passwords dates back to early computer security guidelines, which aimed to reduce the time attackers could exploit stolen credentials by frequently changing passwords. This practice helps mitigate risks associated with credential stuffing, password reuse, and prolonged exposure of compromised passwords. By regularly changing passwords, the time a compromised password can be used is limited, old passwords exposed in breaches are rendered invalid, and regulatory compliance is maintained. Furthermore, frequent changes encourage security awareness among users, reminding them to stay vigilant against phishing and other threats.
To streamline the process of password rotation, various tools and techniques can be employed. Automated password management solutions can schedule and enforce password changes, ensuring compliance with security policies. Additionally, password managers can securely store and generate complex passwords, making it easier for users to adhere to rotation practices without compromising convenience. Implementing multi-factor authentication (MFA) alongside password rotation can further enhance security by adding an extra layer of protection against unauthorized access. By adopting these best practices and utilizing appropriate tools, organizations and individuals can effectively strengthen their cybersecurity posture and safeguard sensitive information.
Profiling of Cafe Business in Talavera, Nueva Ecija: A Basis for Development ...IJAEMSJORNAL
This study aimed to profile the coffee shops in Talavera, Nueva Ecija, to develop a standardized checklist for aspiring entrepreneurs. The researchers surveyed 10 coffee shop owners in the municipality of Talavera. Through surveys, the researchers delved into the Owner's Demographic, Business details, Financial Requirements, and other requirements needed to consider starting up a coffee shop. Furthermore, through accurate analysis, the data obtained from the coffee shop owners are arranged to derive key insights. By analyzing this data, the study identifies best practices associated with start-up coffee shops’ profitability in Talavera. These findings were translated into a standardized checklist outlining essential procedures including the lists of equipment needed, financial requirements, and the Traditional and Social Media Marketing techniques. This standardized checklist served as a valuable tool for aspiring and existing coffee shop owners in Talavera, streamlining operations, ensuring consistency, and contributing to business success.
CS8651- Unit 2 - JS.internet programming paper anna university -2017 regulation
Accelerating Deep Learning Inference on Mobile Systems
1. Accelerating Deep Learning Inference
on Mobile Systems
Darian Frajberg
Carlo Bernaschina
Christian Marone
Piero Fraternali
June 27, 2019
2. 2
Typical implementations of Deep Learning (DL) models focus on
the maximization of accuracy for a given task.
Architectures to achieve such an objective have become
significantly deeper and more complex over time.
Top-5 error (%)
Introduction
3. 3
Artificial Intelligence (AI) on the edge is a
matter of great importance towards the
enhancement of smart devices that rely on
operations with real-time constraints.
Despite the rapid growth of computational
power in embedded systems, such as
smartphones, wearable devices, drones and
FPGAs, the deployment of highly complex and
considerably big DL models remains
challenging.
Introduction
5. 5
Related work
• Compression techniques.
– Quantization
– Pruning
– Knowledge distillation
– Tensor decomposition
• Optimized model architectures.
– SqueezeNet
– MobileNet v1
– MobileNet v2
– MnasNet
• Hardware acceleration.
– Neural Networks API
– OpenGL
– Vulkan
– Metal
6. 6
Related work
• Heterogeneous computing scheduling.
– Mobile GPU
– Custom implementations with access to hardware
primitives
• Mobile Deep Learning frameworks.
– TensorFlow Lite
– Caffe2
– CoreML
7. 7
Limitations
1. Hardware Acceleration primitives are still not
completely standardized and stable, but are
tightly dependent on SoC vendors.
2. Retraining or modifying the architecture of ready-
to-use models can be extremely time-consuming.
3. Post-training compression of already small
models can detriment accuracy.
8. 8
Use case
PeakLens is a real world mobile app that combines Augmented
Reality and Computer Vision (CV) for the identification of mountain
peaks.
It processes sensor readings and camera frames in real-time by
using an efficient on-board Deep Learning-powered CV module.
+400k installs
in Android
9. 9
Requirements
1. Focus on execution. It should be possible to train a model using tools already known
to the developer. The framework should focus just on execution concerns, without the
need of re-training.
2. Minimum dependencies. It should be possible to execute an optimized model
independently of the Operating System, hardware platform or model storage format.
3. Easy embedding. It should be possible to embed the framework and optimized models
into existing applications easily, without the need of ad-hoc integration procedures.
4. End-to-end optimization. Optimization should be applied as early as possible and
span the model life-cycle (generation, compilation, initialization, configuration,
execution).
5. Offline support. Computation should occur only on-board the embedded system,
without the need of a network connection for work off-loading.
6. No accuracy loss. The acceleration for constrained devices should not reduce
accuracy w.r.t. to the execution on a high performance infrastructure.
10. 10
The PolimiDL Framework
PolimiDL is an open source framework for
accelerating DL inference on mobile and embedded
systems, which was started when no efficient off-
the-shelf edge solutions were available.
Implementation is generic and aims at supporting
devices with limited power and heterogeneous
architectures.
12. 12
The PolimiDL Framework
• Generation-time optimizations.
– Layers fusion.
Consecutive in-place layers with identical filter size
can be fused into one single layer, thus reducing the
number of iterations over the cells of an input matrix.
Examples:
• Bias + ReLU = Bias_ReLU
• Batch_Normalization + ReLu6 =
BatchNormalization_ReLU6
13. 13
The PolimiDL Framework
• Generation-time optimizations.
– Weights fusion.
Layers applying functions with constant terms comprising multiple
weights can be pre-computed and encoded as unique constant weights,
thus reducing operations at run-time and potential temporary memory
allocation.
Example:
• Batch Normalization (BN)
14. 14
The PolimiDL Framework
• Generation-time optimizations.
– Weights rearrangement.
Weights associated to predefined Convolutional layer types are
stored in an order such that Eigen’s GEMM matrix operations
do not require any memory reshaping at run-time.
16. 16
The PolimiDL Framework
• Compile-time optimizations.
– Fixed network architecture.
The architecture of a model is fixed at compile-time,
which enables the compiler to perform per-layer
optimizations.
.SO
19. 19
The PolimiDL Framework
• Initialization-time optimizations.
– Memory pre-allocation.
Memory requirements can be reduced by fusing the 3
buffers into a single one. During initialization, each
layer is queried about its memory size requirements.
Layer
input
Layer
output
Temporary
data
20. 20
The PolimiDL Framework
• Initialization-time optimizations.
– Small tasks for low memory consumption.
The operation of certain layers is divided into smaller
tasks that can be executed independently, thus not
performing a complete input unroll, but maintaining a
fixed required size for the temporary memory.
Task
T0 T1 T2 T3 T4
T5 T6 T7 T8 T9
T10 T11 T12 T13 T14
T15 T16 T17 T18 T19
T20 T21 T22 T23 T24
22. 22
The PolimiDL Framework
• Configuration-time optimizations.
– Scheduling optimization.
The optimal size for a scheduled task may vary
depending on the specific layer, the underlying
architecture, or even on the input size for Fully
Convolutional Neural Networks.
The size can be:
• Set to a default value.
• Inferred by executing a profiling routine.
• Loaded from previous profiling routine executions.
24. 24
The PolimiDL Framework
• Run-time optimizations.
– Dynamic workload scheduling.
Dynamic multithreaded scheduling of tasks can adapt
well to different contexts such as ARM big.LITTLE
architecture and allows cores to be better exploited.
25. 25
The PolimiDL Framework
Layers coverage
Layer name In place Temp.
memory
Schedulable
Convolution X √ √
Depthwise convolution X √ √
Pointwise convolution
(out_channels <= in_channels)
√ √ √
Pointwise convolution
(out_channels > in_channels)
X X √
Max Pooling X √ X
Average Pooling X √ √
Batch normalization √ X √
Bias √ X X
ReLU/ReLU6 √ X X
29. 29
Experimental results
Device TensorFlow Lite (ms) PolimiDL (ms)
Asus Zenfone 2 1672.67 1138.00 (-31.96%)
Google Pixel 255.33 171.00 (-33.03%)
LG G5 SE 290.00 209.00 (-27.93%)
LG Nexus 5X 370.33 342.33 (-7.56%)
Motorola Nexus 6 505.33 215.67 (-57.32%)
One Plus 6T 144.33 91.00 (-36.95%)
Average (-32.46%)
PeakLens original
30. 30
Experimental results
Device TensorFlow Lite (ms) PolimiDL (ms)
Asus Zenfone 2 807.67 179.33 (-77.80%)
Google Pixel 95.00 35.33 (-62.81%)
LG G5 SE 138.33 68.00 (-50.84%)
LG Nexus 5X 193.00 80.33 (-58.38%)
Motorola Nexus 6 225.67 66.00 (-70.75%)
One Plus 6T 68.67 22.67 (-66.99%)
Average (-64.59%)
PeakLens optimized
31. 31
Experimental results
Device TensorFlow Lite (ms) PolimiDL (ms)
Asus Zenfone 2 775.33 377.33 (-51.33%)
Google Pixel 82.33 82.67 (+0.40%)
LG G5 SE 274.67 259.00 (-5.70%)
LG Nexus 5X 225.00 234.33 (+4.15%)
Motorola Nexus 6 298.33 176.00 (-41.01%)
One Plus 6T 56.67 51.67 (-8.82%)
Average (-17.05%)
MobileNet v1
32. Concept
– Open source framework for accelerating Deep Learning
inference on mobile and embedded systems, which has
proved competitive w.r.t. TensorFlow Lite.
Future work
– Extended support for more layers, quantization and
conversion from more DL frameworks.
– Extended evaluation with more configurations, metrics
and devices.
32
Conclusions
33. 33
Thanks For Your
Attention!
Accelerating Deep Learning
Inference on Mobile Systems
Darian Frajberg
Carlo Bernaschina
Christian Marone
Piero Fraternali
https://github.com/darianfrajberg/polimidldarian.frajberg@polimi.it
Editor's Notes
Compression techniques target large scale architectures and aim at reducing the number of parameters and floating point operations (FLOPs), possibly tolerating small accuracy drops in favor of execution acceleration and optimization of computational resources, storage, memory occupation and energy consumption.
Lightweight architectures with compact layers pursue the design of an optimized network topology, yielding small, fast and accurate models, suitable for resource-constrained devices.
HA is the use of dedicated hardware to complement general-purpose CPUs and perform computationally intensive work more efficiently, e.g. by favoring specific operations and data-parallel computation.
Heterogeneous computing scheduling comprises the design of strategies to efficiently coordinate and distribute the workload among processors of different types.
Frameworks for the execution of DL models on mobile and embedded systems pursue optimized deployment on devices with limited resources, by managing memory allocation efficiently and exploiting the available hardware resources at best.
Optimized execution requires managing memory allocation efficiently, to avoid overloading, and exploiting the available hardware resources for acceleration, which is not trivial given the non standardized access to such resources.
Evaluation exploits hardware with limited resources and models with a small-size architecture achieving a good trade-o between accuracy and latency. Three models with diverse characteristics, listed in Table 2, are evaluated.