Tabla de Contenidos: “…2.2 Hardware Trade-offs2.2.1 Performance Increase with Frequency, and its Limitations; 2.2.2 Superscalar Execution; 2.2.3 Very Long Instruction Word; 2.2.4 SIMD and Vector Processing; 2.2.5 Hardware Multithreading; 2.2.6 Multicore Architectures; 2.2.7 Integration: Systems-on-Chip and the APU; 2.2.8 Cache Hierarchies and Memory Systems; 2.3 The Architectural Design Space; 2.3.1 CPU Designs; Low-power CPUs; Mainstream desktop CPUs; Server CPUs; 2.3.2 GPU Architectures; Handheld GPUs; At the high end: AMD Radeon R9 290X and NVIDIA GeForce GTX 780; 2.3.3 APU and APU-like Designs; 2.4 Summary…”

Tabla de Contenidos: “…Preface -- 1 Introduction -- 1.1 Mobile 3D Graphics -- 1.2 Mobile Devices and Design Challenges -- 1.2.1 Mobile Computing Power -- 1.2.2 Mobile Display Devices -- 1.2.3 Design Challenges -- 1.3 Introduction to SoC Design -- 1.4 About this Book -- 2 Application Platform -- 2.1 SoC Design Paradigms -- 2.1.1 Platform and Set-based Design -- 2.1.2 Modeling: Memory and Operations -- 2.2 System Architecture -- 2.2.1 Reference Machine and API -- 2.2.2 Communication Architecture Design -- 2.2.3 System Analysis -- 2.3 Low-power SoC Design -- 2.3.1 CMOS Circuit-level Low-power Design -- 2.3.2 Architecture-level Low-power Design -- 2.3.3 System-level Low-power Design -- 2.4 Network-on-Chip based SoC -- 2.4.1 Network-on-Chip Basics -- 2.4.2 NoC Design Considerations -- 2.4.3 Case Studies of Chip Implementation -- 3 Introduction to 3D Graphics -- 3.1 The 3D Graphics Pipeline -- 3.1.1 The Application Stage -- 3.1.2 The Geometry Stage -- 3.1.3 The Rendering Stage -- 3.2 Programmable 3D Graphics -- 3.2.1 Programmable Graphics Pipeline -- 3.2.2 Shader Models -- 4 Mobile 3D Graphics -- 4.1 Principles of Mobile 3D Graphics -- 4.1.1 Application Challenges -- 4.1.2 Design Principles -- 4.2 Mobile 3D Graphics APIs -- 4.2.1 KAIST MobileGL -- 4.2.2 Khronos OpenGL-ES -- 4.2.3 Microsoft's Direct3D-Mobile -- 4.3 Summary and Future Directions -- 5 Mobile 3D Graphics SoC -- 5.1 Low-power Rendering Processor -- 5.1.1 Early Depth Test -- 5.1.2 Logarithmic Datapaths -- 5.1.3 Low-power Texture Unit -- 5.1.4 Tile-based Rendering -- 5.1.5 Texture Compression -- 5.1.6 Texture Filtering and Anti-aliasing -- 5.2 Low-power Shader -- 5.2.1 Vertex Cache -- 5.2.2 Low-power Register File -- 5.2.3 Mobile Unified Shader -- 6 Real Chip Implementations -- 6.1 KAIST RAMP Architecture -- 6.1.1 RAMP-IV -- 6.1.2 RAMP-V -- 6.1.3 RAMP-VI -- 6.1.4 RAMP-VII -- 6.2 Industry Architecture -- 6.2.1 nVidia Mobile GPU - SC10 and Tegra -- 6.2.2 Sony PSP -- 6.2.3 Imagination Technology MBX/SGX -- 7 Low-power Rasterizer Design…”

Tabla de Contenidos: “…-- ANNs and the backpropagation algorithm -- Weight optimization -- Stochastic gradient descent -- Neural network architectures -- Deep Neural Networks (DNNs) -- Multilayer perceptron -- Deep Belief Networks (DBNs) -- Convolutional Neural Networks (CNNs) -- AutoEncoders -- Recurrent Neural Networks (RNNs) -- Emergent architectures -- Deep learning frameworks -- Summary -- Chapter 2: A First Look at TensorFlow -- A general overview of TensorFlow -- What's new in TensorFlow v1.6? -- Nvidia GPU support optimized -- Introducing TensorFlow Lite -- Eager execution -- Optimized Accelerated Linear Algebra (XLA) -- Installing and configuring TensorFlow -- TensorFlow computational graph -- TensorFlow code structure -- Eager execution with TensorFlow -- Data model in TensorFlow -- Tensor -- Rank and shape -- Data type -- Variables -- Fetches -- Feeds and placeholders -- Visualizing computations through TensorBoard -- How does TensorBoard work? …”

Tabla de Contenidos: “…Front Cover -- Half Title page -- RIVER PUBLISHERS SERIES IN INFORMATIONSCIENCE AND TECHNOLOGY -- Title page -- Copyright page -- Contents -- Preface -- List of Contributors -- List of Figures -- List of Tables -- List of Abbreviations -- Chapter 1 - Introduction -- 1.1 Introduction -- 1.1.1 The Convergence of High-performance and Embedded Computing Domains -- 1.1.2 Parallelization Challenge -- 1.2 The P-SOCRATES Project -- 1.3 Challenges Addressed in This Book -- 1.3.1 Compiler Analysis of Parallel Programs -- 1.3.2 Predictable Scheduling of Parallel Tasks on Many-core Systems -- 1.3.3 Methodology for Measurement-based Timing Analysis -- 1.3.4 Optimized OpenMP Tasking Runtime System -- 1.3.5 Real-time Operating Systems -- 1.4 The UpScale SDK -- 1.5 Summary -- References -- Chapter 2 - Manycore Platforms -- 2.1 Introduction -- 2.2 Manycore Architectures -- 2.2.1 Xeon Phi -- 2.2.2 Pezy SC -- 2.2.3 NVIDIA Tegra X1 -- 2.2.4 Tilera Tile -- 2.2.5 STMicroelectronics STHORM -- 2.2.6 Epiphany-V -- 2.2.7 TI Keystone II -- 2.2.8 Kalray MPPA-256 -- 2.2.8.1 The I/O subsystem -- 2.2.8.2 The Network-on-Chip (NoC) -- 2.2.8.3 The Host-to-IOS communication protocol -- 2.2.8.4 Internal architecture of the compute clusters -- 2.2.8.5 The shared memory -- 2.3 Summary -- References -- Chapter 3 - Predictable Parallel Programming with OpenMP -- 3.1 Introduction -- 3.1.1 Introduction to Parallel Programming Models -- 3.1.1.1 POSIX threads -- 3.1.1.2 OpenCLTM -- 3.1.1.3 NVIDIA R CUDA -- 3.1.1.4 Intel R CilkTM Plus -- 3.1.1.5 Intel R TBB -- 3.1.1.6 OpenMP -- 3.2 The OpenMP Parallel Programming Model -- 3.2.1 Introduction and Evolution of OpenMP -- 3.2.2 Parallel Model of OpenMP -- 3.2.2.1 Execution model -- 3.2.2.2 Acceleration model -- 3.2.2.3 Memory model -- 3.2.3 An OpenMP Example -- 3.3 Timing Properties of the OpenMP Tasking Model…”

Tabla de Contenidos: “…-- Popular alternatives to TensorFlow -- GPU requirements for TensorFlow and Keras -- Installing Nvidia CUDA Toolkit and cuDNN -- Installing Python -- Installing TensorFlow and Keras -- Building datasets for deep learning -- Bias and variance errors in deep learning -- The train, val, and test datasets -- Managing bias and variance in deep neural networks -- K-Fold cross-validation -- Summary -- Chapter 2: Using Deep Learning to Solve Regression Problems -- Regression analysis and deep neural networks -- Benefits of using a neural network for regression -- Drawbacks to consider when using a neural network for regression -- Using deep neural networks for regression -- How to plan a machine learning problem -- Defining our example problem -- Loading the dataset -- Defining our cost function -- Building an MLP in Keras -- Input layer shape -- Hidden layer shape -- Output layer shape -- Neural network architecture -- Training the Keras model -- Measuring the performance of our model -- Building a deep neural network in Keras -- Measuring the deep neural network performance -- Tuning the model hyperparameters -- Saving and loading a trained Keras model -- Summary…”

Tabla de Contenidos: “…Front Cover -- Multicore and GPU Programming: An Integrated Approach -- Copyright -- Dedication -- Contents -- List of Tables -- Preface -- What Is in This Book -- Using This Book as a Textbook -- Software and Hardware Requirements -- Sample Code -- Chapter 1: Introduction -- 1.1 The era of multicore machines -- 1.2 A taxonomy of parallel machines -- 1.3 A glimpse of contemporary computing machines -- 1.3.1 The cell BE processor -- 1.3.2 Nvidia's Kepler -- 1.3.3 AMD's APUs -- 1.3.4 Multicore to many-core: tilera's TILE-Gx8072 and intel's xeon phi -- 1.4 Performance metrics -- 1.5 Predicting and measuring parallel program performance -- 1.5.1 Amdahl's law -- 1.5.2 Gustafson-barsis's rebuttal -- Exercises -- Chapter 2: Multicore and parallel program design -- 2.1 Introduction -- 2.2 The PCAM methodology -- 2.3 Decomposition patterns -- 2.3.1 Task parallelism -- 2.3.2 Divide-and-conquer decomposition -- 2.3.3 Geometric decomposition -- 2.3.4 Recursive data decomposition -- 2.3.5 Pipeline decomposition -- 2.3.6 Event-based coordination decomposition -- 2.4 Program structure patterns -- 2.4.1 Single-program, multiple-data -- 2.4.2 Multiple-program, multiple-data -- 2.4.3 Master-worker -- 2.4.4 Map-reduce -- 2.4.5 Fork/join -- 2.4.6 Loop parallelism -- 2.5 Matching decomposition patterns with program structure patterns -- Exercises -- Chapter 3: Shared-memory programming: threads -- 3.1 Introduction -- 3.2 Threads -- 3.2.1 What is a thread? …”

Tabla de Contenidos: “…11.6.2 AI, behavioral science, and mechanism design for GDPR and cross-border data flow -- 11.7 Global treaty on cross-border data free flow -- 11.8 Conclusion -- AI disclosure -- References -- 12 - Synthetic data -- 12.1 Introduction -- 12.2 Synthetic data -- 12.3 Generative adversarial networks -- 12.4 Cost saving -- 12.5 Time saving -- 12.6 Privacy protection -- 12.7 Improved model performance -- 12.8 Data quality -- 12.9 Model explainability -- 12.10 Legal and ethics -- 12.11 Governance of synthetic data -- 12.12 Behavioral science and mechanism design in synthetic data -- 12.13 Conclusion -- AI disclosure -- References -- 13 - Computer chips -- 13.1 Introduction -- 13.2 Digital computing -- 13.3 Transistor -- 13.4 Integrated circuit -- 13.5 Moore's law -- 13.6 From CPU to GPU -- 13.7 NVIDIA chips -- 13.8 Quantum computing -- 13.9 AI, mechanism design, and behavioral science in computer chips -- 13.10 Computer chips and international relations -- 13.11 Conclusion -- AI disclosure -- References -- Further reading -- 14 - Space -- 14.1 Introduction -- 14.2 Communication -- 14.3 Earth observation -- 14.4 Navigation -- 14.5 Military -- 14.6 Space technology and AI -- 14.7 Starlink -- 14.8 Mechanism design and behavioral science -- 14.9 Space and international relations -- 14.10 Space governance -- 14.11 Conclusion -- AI disclosure -- References -- III - Sustainability -- 15 - Climate change -- 15.1 Introduction -- 15.2 What is climate change? …”

Tabla de Contenidos: “…3.3.4 Installation abschließen und neu starten -- 3.4 Dual-Boot - Kali Linux und Windows -- 3.5 Installation auf einem vollständig verschlüsselten Dateisystem -- 3.5.1 Einführung in LVM -- 3.5.2 Einführung in LUKS -- 3.5.3 Konfigurieren verschlüsselter Partitionen -- 3.6 Kali Linux auf Windows Subsystem for Linux -- 3.6.1 Win-KeX‌ -- 3.7 Kali Linux auf einem Raspberry Pi -- 3.8 Systemeinstellungen und Updates -- 3.8.1 Repositories -- 3.8.2 NVIDIA-Treiber für Kali Linux installieren -- 3.8.3 Terminal als Short-Cut (Tastenkombination) -- 3.9 Fehlerbehebung bei der Installation‌ -- 3.9.1 Einsatz der Installer-Shell zur Fehlerbehebung -- 3.10 Zusammenfassung -- Kapitel 4: Erste Schritte mit Kali -- 4.1 Konfiguration von Kali Linux -- 4.1.1 Netzwerkeinstellungen -- 4.1.2 Verwalten von Benutzern und Gruppen -- 4.1.3 Services konfigurieren -- 4.2 Managing Services -- 4.3 Hacking-Labo‌r einrichten -- 4.3.1 Kali Linux - Test Lab Environment -- 4.4 Sichern und Überwachen mit Kali Linux -- 4.4.1 Sicherheitsrichtlinie‌n definieren -- 4.4.2 Mögliche Sicherheitsmaßnahmen -- 4.4.3 Netzwerkservices absichern -- 4.4.4 Firewall- oder Paketfilterung -- 4.5 Weitere Tools installieren -- 4.5.1 Meta-Packages mit kali-tweaks‌ installieren -- 4.5.2 Terminator statt Terminal -- 4.5.3 OpenVAS zur Schwachstellenanalyse -- 4.5.4 SSLstrip2 -- 4.5.5 Dns2proxy‌ -- 4.6 Kali Linux ausschalten -- 4.7 Zusammenfassung -- Teil II: Einführung in Penetration Testing -- Kapitel 5: Einführung in Security Assessments -- 5.1 Kali Linux in einem Assessment -- 5.2 Arten von Assessments -- 5.2.1 Schwachstellenanalyse -- 5.2.2 Compliance-Test -- 5.2.3 Traditioneller Penetrationstest -- 5.2.4 Applikations-Assessment -- 5.3 Normierung der Assessments -- 5.4 Arten von Attacken -- 5.4.1 Denial of Service‌s (D‌oS) -- 5.4.2 Speicherbeschädigungen -- 5.4.3 Schwachstellen von Webseiten…”

Tabla de Contenidos: “…. -- See also -- Writing massively parallel code for NVIDIA graphics cards (GPUs) with CUDA -- Getting ready -- How to do it... -- How it works...…”

Tabla de Contenidos: “…KDE Plasma general keyboard shortcuts -- GNOME edition and settings -- The other editions - Cinnamon, Lxde, Mate, and so on -- Summary -- Chapter 4: Help, Online Resources, Forums, and Updates -- Help -- Troubleshooting -- Online resources -- The news -- The forum - the greatest collection of knowledge -- Notices -- Introduce Yourself -- Non-technical Questions -- Support -- ARM -- Announcements -- Manjaro Development -- Contributions -- Languages -- Feedback -- The Rolling Release Development Model -- Updates -- Summary -- Part 2: Daily Usage -- Chapter 5: Officially Supported Software - Part 1 -- Pamac - the Add/Remove SW GUI application -- The Flatpak, Snap, and AppImage containers -- For advanced users -- AUR -- The Manjaro repositories -- Office tools, calendars, and mail clients -- Mail clients with calendars -- Office -- Browsers -- Test results -- Photo, video, image, and graphics -- Photo/images -- Graphics -- Summary -- Chapter 6: Officially Supported Software Part 2, 3D Games, and Windows SW -- Music and audio -- Classic audio players -- Streaming players -- Audio editors and DAWs -- Music servers -- Teams, Zoom, Viber, Spotify, WhatsApp, Signal, and Telegram -- A note regarding Signal, Telegram, and WhatsApp -- Text editors -- Lightweight/simple editors -- Sophisticated editors -- IDEs -- Terminal editors -- Recommendations -- Drivers, tools, and simple games -- Drivers on Manjaro Linux -- NVIDIA, open source, and other hardware drivers -- Screenshot tools -- Virtual machines -- Education and Science -- For video and/or audio conversion -- Others -- Simple Games -- Advanced 2D/3D game support on Linux -- Game controller support and further points -- For advanced users -- Creating application shortcuts, and converting .deb and .rpm packages -- Converting .deb and .rpm packages -- How to use Windows software on Linux…”

Tabla de Contenidos: “…-- 12.11 Triangles -- Theory -- Implementation -- 12.12 Lights -- Implementation -- 12.13 Textures -- Theory -- Implementation -- 3 Appendices -- A System and environment management -- A.1 Environment variables -- A.1.1 General -- A.1.2 Just-in-time compilation -- A.2 nvidia-smi - System Management Interface -- A.2.1 Enabling and disabling ECC -- A.2.2 Compute mode -- A.2.3 Persistence mode -- A.2.4 Topology -- References -- Index -- Back Cover…”

Tabla de Contenidos: “…3.3.1 Debian-‌Backports - aktuellere Software aus dem nächsten Debian-Release installieren -- 3.3.2 ‌Pinning - ‌aktuellere Software aus anderen Debian-Versionen installieren -- 3.3.3 Auf ‌Debian Testing wechseln -- 3.4 Weitere Paket-Formate für zusätzliche Software -- 3.4.1 ‌Flatpak - zusätzliche und aktuellere Software -- 3.4.2 ‌Snap - zusätzliche und aktuellere Software von Ubuntu -- 3.5 ‌AppImages - ausführbare Dateien unter Linux -- 3.6 Software für die Programmierung -- 3.6.1 ‌Python-Software über das Python-Repository verwalten -- 3.6.2 ‌Rust-Software mittels ‌Cargo verwalten -- 3.7 ‌Software unter Debian ‌kompilieren -- 3.7.1 ‌C-Programme kompilieren -- 3.7.2 ‌C++-Programme kompilieren -- 3.8 ‌Distrobox - Software von anderen Linux-Distributionen im Container installieren -- 3.9 Software über ‌selbst extrahierende Skripte installieren -- Kapitel 4: Das System -- 4.1 Die ‌Verzeichnis-Hierarchie -- 4.1.1 Das Wurzelverzeichnis -- 4.1.2 Das ‌Home-Verzeichnis -- 4.2 ‌Rechte an Ihren Daten - ‌Gruppen -- 4.2.1 ‌Benutzer und Gruppen ‌erstellen -- 4.3 ‌sudo und ‌su - ‌der ‌Administrator unter Debian -- 4.4 ‌Das ‌Terminal - die ‌Kommandozeile -- 4.4.1 ‌Der Aufbau des Terminals und dessen Grundlagen -- 4.4.2 ‌Ordner-Inhalte anzeigen und in der Verzeichnis-Hierarchie navigieren -- 4.4.3 ‌Hilfe und Optionen -- 4.4.4 ‌Arbeiten mit Dateien und Ordnern am Terminal -- 4.4.5 ‌Kopieren und ‌Einfügen -- 4.5 Treiber und Firmware -- 4.5.1 ‌Druckertreiber und ‌Scannertreiber -- 4.5.2 ‌NVIDIA-‌Grafikkarten-Treiber installieren -- 4.6 ‌Systemd - die Steuerzentrale von Debian -- 4.6.1 Installierte ‌Systemdienste anzeigen lassen -- 4.6.2 Nähere Informationen zu einem Systemdienst anzeigen -- 4.6.3 Dienste manuell starten und stoppen -- 4.6.4 Mit Systemd Debian steuern (ausschalten, neu starten, ...) -- 4.6.5 ‌Log-Dateien anzeigen -- 4.6.6 ‌Start-Analyse mit Systemd…”

Tabla de Contenidos: “…9.9.3 Komplexer Prompt für cineastische Szenerie‌ -- Kapitel 10: KI-gestützte Audio-Produktion und Erstellung von Podcasts -- 10.1 Gründe für den Einsatzes von KI-Audio-Tools -- 10.2 Bearbeitungsschritte beim Einsatz von KI-Audio-Tools -- 10.3 Erfahrung aus der Produktion des Podcasts »Die Digitalisierung und Wir« -- 10.3.1 Werkzeuge für die Podcast-Produktion -- 10.4 KI-Tools mit Fokus auf Podcast-Produktion‌en -- 10.4.1 Riverside‌: Eine KI-gestützte Podcast-Plattform -- 10.4.2 Resound‌: KI-gestütztes Podcast-Editing-Tool -- 10.4.3 Podcastle‌: Plattform für KI-gestützte Audioaufnahme und -bearbeitung -- 10.4.4 Castmagic‌: KI-Plattform für Podcast-Produktion -- 10.4.5 Adobe Podcast‌: Ein umfassendes Werkzeug für Podcaster -- 10.5 Automatische Transkription‌ durch KI -- 10.5.1 Descript‌: Textbasierte Bearbeitung von Podcasts‌, Audio- und Videoinhalten -- 10.5.2 NOVA AI‌: Transkription und Untertitelerzeugung -- 10.6 Künstliche Intelligenz zur Musikproduktion -- 10.6.1 Amper Music‌: Benutzerfreundliche KI-gestützte Musikkomposition -- 10.6.2 AIVA‌: KI-gestützte Komposition und Anpassung von Soundtracks -- 10.6.3 Soundful‌: KI-generierte, lizenzfreie Hintergrundmusik‌ -- 10.6.4 Ecrett Music‌: KI-Training für vielseitige Musikkomposition -- 10.7 Künstliche Intelligenz für Spracherzeugung‌ -- 10.7.1 Murf AI‌: Ein vielseitiges Werkzeug für KI-basierte Spracherzeugung -- 10.7.2 Lovo.ai Genny‌: Ein umfassender KI-Sprachgenerator für eine Vielzahl von Anwendungen -- 10.7.3 Speechify Voice Over‌: Ein alternativer Ansatz zur KI-Spracherzeugung -- 10.8 Weitere nennenswerte Audio-KI-Tools -- 10.8.1 Auphonic‌: KI-gestützte Audioverbesserung‌ -- 10.8.2 NVIDIA Jarvis‌: KI-basierte Konversationsplattform‌ -- 10.9 Fazit zu KI-gestützten Audio-Tools -- Kapitel 11: KI-gestützte Videoproduktion und Werbefilmproduktion…”

“…Major topics covered include: Geometric Complexity Shading, Lighting, and Shadows High-Quality Rendering General-Purpose Computation on GPUs: A Primer Image-Oriented Computing Simulation and Numerical Algorithms Contributors are from the following corporations and universities: 1C: Maddox Games 2015 Apple Computer Armstrong State University Climax Entertainment Crytek discreet ETH Zurich GRAVIR/IMAG—INRIA GSC Game World Lionhead Studios Lund University Massachusetts Institute of Technology mental images Microsoft Research NVIDIA Corporation Piranha Bytes Siemens Corporate Research Siemens Medical Solutions Simutronics Corporation Sony Pictures Imageworks Stanford University Stony Brook University Technische Universität München University of California, Davis University of North Carolina at Chapel Hill University of Potsdam University of Tokyo University of Toronto University of Utah University of Virginia University of Waterloo Vienna University of Technology VRVis Research Center Section editors include NVIDIA engineers: Kevin Bjorke, Cem Ceben..…”

“…For using GPU technologies, you'll need an NVIDIA graphics card compatible with NVIDIA's RAPIDS library, which is compatible with Windows 10 and Linux…”

“…Learn what deep learning neural networks are, what they're used for, and why they're powerful Discover the particular structure of neural networks and why it matters Explore the basic concepts used in building and training neural networks Develop a solid platform for learning more about deep learning and neural networks Laura Graesser is assisting with NVIDIA's autonomous driving project. Previously with The Boston Consulting Group, Laura is a graduate student at New York University, where she's working toward a master’s degree in computer science and machine learning. …”

“…This year's panelists were Francois Chretien (Adobe), Emil Praun (Google), Michael Garland (NVIDIA), and Tony DeRose (Pixar)…”

“…By providing a new level of abstraction, Cg lets developers more directly target OpenGL®, DirectX®, Windows®, Linux, Mac OS X®, and console platforms, such as the XboxTM, without having to program directly to the graphics hardware assembly language. Cg was developed by NVIDIA® Corporation in close collaboration with Microsoft® Corporation, and is compatible with both the OpenGL API and Microsoft's HLSL for DirectX 9.0. …”

“…Foreword by Ting-Chun Wang, Senior Research Scientist, NVIDIA What you will learn Discover how GANs work and the advantages and challenges of working with them Control the output of GANs with the help of conditional GANs, using embedding and space manipulation Apply GANs to computer vision, natural language processing (NLP), and audio processing Understand how to implement progressive growing of GANs Use GANs for image synthesis and speech enhancement Explore the future of GANs in visual and sonic arts Implement pix2pixHD to turn semantic label maps into photorealistic images Who this book is for This book is for machine learning practitioners, deep learning researchers, and AI enthusiasts who are looking for a mix of theory and hands-on co..…”

“…This book covers deployment of OpenCV applications on NVIDIA Jetson Tx1 which is very popular for computer vision and deep learning applications. …”