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2018 GTC Washington DC
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Browse & Search for sessions, and click "Add to My Interests" to save sessions to your interest list.

Check back frequently as new sessions will be added. Session scheduler will be available to build your personalized conference schedule in early September.

Featured Sessions

DC8152 - Accelerated-Node Software Technologies and Applications in the U.S. Department of Energy Exascale Computing Project The vision of the Exascale Computing Project, initiated in 2016 as a formal U.S. Department of Energy project executing through 2022, is to accelerate innovation with exascale simulation and data science solutions. After a brief overview of this, we will give illustrative examples on how the ECP teams are leveraging, exploiting, and advancing accelerated-node software technologies and applications on hardware such as the powerful GPUs provided by NVIDIA. We will summarize best practices and lessons learned from these accelerated-node experiences along with ECP's plans moving into the exascale era, which is on the now near-term horizon. These solutions will enhance U.S. economic competitiveness, change our quality of life, and strengthen our national security. ECP's mission is to deliver exascale-ready applications and solutions that address currently intractable problems of strategic importance and national interest; create and deploy an expanded and vertically integrated software stack on DOE HPC exascale and pre-exascale systems, defining the enduring US exascale ecosystem; and leverage U.S. HPC vendor research activities and products into DOE HPC exascale systems. The project is a joint effort of two DOE programs: the Office of Science Advanced Scientific Computing Research Program and the National Nuclear Security Administration Advanced Simulation and Computing Program. ECP's RD&D activities, which encompass the development of applications, software technologies, and hardware technologies and architectures, is carried out by over 100 small teams of scientists and engineers from the DOE national laboratories, universities, and industries. Talk Doug Kothe - Director, Exascale Computing Project, Oak Ridge National Lab
DC8156 - Accelerate Video Analytics Development with DeepStream 2.0 This talk explores how DeepStream enables developers to create high-stream density applications with deep learning and accelerated multimedia image processing, building IVA solutions at scale. Leverage a heterogeneous concurrent neural network architecture to bring in different deep learning techniques for more intelligent insights. The framework makes it easy to create flexible and intuitive graph-based applications, resulting in highly optimized pipelines for maximum throughput. Talk Saurabh Jain - Business Development, NVIDIA
DC8162 - Accelerating Research to Production with PyTorch 1.0 and ONNX (Presented by Facebook) Facebook's strength in AI innovation comes from its ability to quickly bring cutting-edge research into large scale production using a multi-faceted toolset. Learn how ONNX and PyTorch 1.0 are helping to accelerate the path from research to production by making AI development more seamless and interoperable. We'll share the latest on PyTorch 1.0 and discuss Facebook's initiatives around ethical and responsible AI development. Talk Sarah Bird - Technical Program Manager, Facebook
DC8175 - Accelerating Understanding: The Convergence of HPC and AI in a Post-Moore Law's World Most AI researchers and industry pioneers agree that the wide availability and low cost of highly-efficient and powerful GPUs and accelerated computing parallel programming tools (originally developed to benefit HPC applications) catalyzed the modern revolution in AI/Deep Learning. Now, AI methods and tools are starting to be applied to HPC applications to great effect. This talk will describe an emergent workflow that uses traditional HPC numeric simulations to generate the labeled data sets required to train machine learning algorithms, then employs the resulting AI models to predict the computed results, often with dramatic gains in efficiency, performance, and even accuracy. Some compelling success stories will be shared, and the implications of this new HPC + AI workflow on HPC applications and system architecture in a post-Moore's Law world considered. Talk Steven Oberlin - Chief Technology Officer, Accelerated Computing, NVIDIA
DC8173 - AI and IoT – How Future Cities Will Be Built Cities are always looking for new ways to maintain high standards of living, better connect with citizens and find ways to save money—all while serving growing populations. As city population densities increase and cities strive to increase walkability and mobility for their citizens, they have a big focus on a holistic approach to traffic safety. As part of their efforts to become smarter, more and more cities are turning to the Internet of Things (IoT) and Machine-to- Machine (M2M) technologies to improve municipal services, create additional sources of revenue, and enable city management in new and creative ways. Talk Csaba Rekeczky - Head of Advanced Analytics & Vision Systems, Verizon
DC8132 - AI at the Edge - Intelligent Machines Artificial intelligence is impacting almost every part of the industrial and agricultural supply chain. From robots that quickly adapt to build new products, to automated vehicles that address last-mile challenges for product delivery, to UAVs that can automatically detect failing infrastructure, the world is transitioning from processes that are largely manual to ones that are largely automated. We'll discuss how AI and deep learning are enabling these advances. We'll also analyze a sampling of early successes across different applications. And finally we'll describe some of the remaining challenges to wide-scale deployment, and the work NVIDIA is doing to address those challenges via its Isaac initiative. Talk Jesse Clayton - Senior Manager, Product Management for Intelligent Machines, NVIDIA
DC8155 - AI Infrastructure for Healthcare Deployments As research and clinical healthcare organizations formulate and Implement AI strategies, a crucial component is planning for the proper AI compute infrastructure. This talk will address compute infrastructure planning in healthcare settings, including reference architectures and best practices that NVIDIA has developed based on our internal AI supercomputer, as well as examples of successful AI deployments by leading healthcare organizations. Talk Pradeep Gupta - Head Solutions Architect, NVIDIA
Jonathan Bentz - Solutions Architect, NVIDIA
DC8149 - AI in the Driver's Seat We will present a survey of the latest AI technology for autonomous vehicle development from training to simulation to testing. Talk Daniel Shapiro - Senior Director, Automotive, NVIDIA
DC8148 - A tour of tf.keras: TensorFlow's high-level API tf.keras is TensorFlow's implementation of the Keras API specification for building and training deep learning models, specifically ones that will automatically use GPUs when available. In this talk, I'll give a tour of the new features and point you to end-to-end code examples you can try for each. This talk assumes prior experience with Keras and/or TensorFlow. Talk Joshua Gordon - Developer Advocate, Google
DC8139 - Attacking the Opioid Epidemic with Exascale Genomics We will describe the CoMet application for largescale epistatic Genome-Wide Association Studies (eGWAS) and pleiotropy studies. High performance is attained by transforming the underlying vector comparison methods into generalized distributed dense linear algebra operations. The 2-way and 3-way Proportional Similarity metric and the Custom Correlation Coefficient are implemented using adapted xGEMM kernels optimized for GPU architectures, achieving instruction rates similar to the unmodified kernels. By aggressive overlapping of communications, transfers and computations, and accessing the tensor cores on the Volta GPU, the full computation achieves up to 95 TF per GPU (76% of tensor cores theoretical peak 125 TF) on Summit. 234 x 10^15 element comparisons and 1.88 ExaOps have been reached on 4000 nodes of Summit; full system Summit projected values are 270 x 10^15 comparisons and over 2 ExaOps. Current performance is over 10,000X beyond comparable state of the art. CoMet is currently being used in projects ranging from bioenergy to clinical genomics, including for the genetics of chronic pain and opioid addiction. Talk Daniel Jacobson - Chief Scientist for Systems Biology, Oak Ridge National Laboratory
DC8110 - Benchmarking Graph Analytics on the DGX-2 This talk will present the results of running the following Graph500 and DARPA Graph Challenge benchmarks and highlight the improvements over other platforms: • BFS Graph500 • Single Source Shortest Paths Graph500 • PageRank Pipeline Graph Challenge • Triangle Counting Graph Challenge • K-Truss Graph Challenge The tremendous performance advantages of the DGX-2 platform for deep-learning has recently gained a lot of publicity. However, that is not the only analytic environment that can take advantage of the DGX-2 architecture. Having sixteen fully connected 32GB Volta GPUs presents an intriguing platform for Graph Analytics. The 512GB of combined GPU memory and full NVLink connection between the GPUs offers a number of advantages over a distributed MPI-based approach. Talk Frank Eaton - Sr Software Engineer, NVIDIA
Bradley Rees - Sr System Engineer, NVIDIA
DC8143 - Building the World's Largest GPU: Get Under the Hood with NVIDIA DGX-2 This technical session will explore the objectives for building the DGX-2, along with the inspired, innovative technology and architecture used to eliminate traditional bottlenecks and to enable multi-GPU training at unprecedented scale. This talk led by the DGX product team will present on the following topics: -the innovation and architecture found in NVSwitch, which enables the AI network fabric for the DGX-2 platform -the design challenges and hardware architecture employed to enable 16 V100's to operate as one -feature by feature walkthrough highlighting the most important innovations that accelerate deep learning workflow and training performance -use cases that were previously unaddressable on a GPU platform, now solved with DGX-2 Talk Charles Boyle - Sr. Director, Product Management, NVIDIA
DC8167 - Building Tools for Artificial Intelligence Research in Medical Imaging The goal of this session is to describe the motivations behind building various tools for Artificial Intelligence Research in an academic Medical Center emphasizing the challenges and practical solutions for successful implementation. The discussion will cover key aspects of the software development cycle in the Healthcare setting including access to data, platforms and infrastructure, collaborative algorithm development, algorithm implementation and integration into clinical workflows. Talk Vikash Gupta - Research Scientist, OSU Wexner Medical Center
Barbaros Erdal - Assistant Professor, OSU Wexner Medical Center
DC8176 - Content, Authorship, and Infrastructure for VR in Educational Setting The potential for XR technologies to transform education is well-recognized. Even so, many practical barriers remain before any of them can be widely adopted in practice. In this talk, I will present a multi-stage pipeline model spanning from conception to adoption of a VR learning experience, noting challenges and approaches relevant to each stage. I will use my own work in K12 (domain-independent project-based learning) and higher education (electricity and magnetism) to exemplify the arguments. Talk Scott Greenwald - Research Scientist, MIT Media Lab, Fluid Interfaces Group
DC8170 - CUDA: New Features and Technologies This session will cover details of new features in the most recent release of CUDA, including updates to libraries, tools and the CUDA language itself. We will also present performance improvements using CUDA on the latest NVIDIA GPUs. Talk Pramod Ramarao - Product Manager, NVIDIA
DC8108 - Deep Learning Demystified What is Deep Learning? In what fields is it useful, and how does it relate to artificial intelligence? During this session, we'll get an understanding of deep learning and why this powerful new technology is getting so much attention. Learn how deep neural networks are trained to perform tasks with super-human accuracy, and the challenges organizations face in adopting this new approach. We'll also cover some of the best practices, software, hardware, and training resources that many organizations are using to overcome these challenges and deliver breakthrough results. Talk William Ramey - Director, Developer Programs, NVIDIA
W219633 - Deep Learning for Full Motion Video Analytics

Prerequisites: Basic familiarity with concepts of deep learning and convolutional neural networks

Traffic cameras, drones, and aerial sensor platforms are collecting huge amounts of video footage, which requires advanced deep learning techniques to transform data into actionable insights. The first step in more complex deep learning workflows is detecting specific types of objects, which involves identification, classification, segmentation, prediction, and recommendation. In this course, you’ll learn how to:

  • Train and evaluate deep learning models using the TensorFlow Object Detection API
  • Explore the strategies and trade-offs involved in developing high-quality neural network models for track moving objects in large-scale video datasets
  • Optimize inference times using TensorRT for real-time applications

Upon completion, you’ll be able to deploy object detection and tracking networks to work on real-time, large-scale video streams.

Pre-GTC DLI Workshops
W219634 - Deep Learning for Healthcare Image Analysis

Prerequisites: Basic familiarity with concepts of deep learning and convolutional neural networks

This hands-on course explores how to apply Convolutional Neural Networks (CNNs) to MRI scans to perform a variety of medical tasks and calculations. You’ll learn how to:

  • Perform image segmentation on MRI images to determine the location of the left ventricle.
  • Calculate ejection fractions by measuring differences between diastole and systole using CNNs applied to MRI scans to detect heart disease.
  • Apply CNNs to MRI scans of LGGs to determine 1p/19q chromosome co-deletion status.

Upon completion of this course, you’ll be able to apply CNNs to MRI scans to conduct a variety of medical tasks.

Pre-GTC DLI Workshops
DC8154 - Deep Learning for RF Sensing and Learned Communications Machine learning is rapidly advancing the state-of-the-art in algorithm performance for wireless telecommunications systems. Building on our work presented at GTC Silicon Valley, recasting fundamental wireless signal processing problems as data-centric deep learning problems, we present further evidence that learned signal processing algorithms can empower the next generation of wireless systems with significant reductions in power consumption and improvements in density, throughput, and accuracy when compared to the brittle and manually designed systems of today. This talk will introduce the core enabling technologies and fundamental approaches, share our latest work and results in deep learning-based sensing and learned communications, and discuss applications such as 5G and IoT, commercial cyber-threat sensing, and defense RF sensing to illustrate the wide range of fields these technologies will impact over the next several years. Talk Ben Hilburn - Director of Engineering, DeepSig Inc.
DC8128 - Deep Learning for Smart Home Monitoring We address the recognition of agent-in-place actions, which are associated with agents who perform them and places where they occur, in the context of outdoor home monitoring. We introduce a representation of the geometry and topology of scene layouts so that a network can generalize from the layouts observed in the training set to unseen layouts in the test set. We introduce the Agent-in-Place Action dataset to show that our method allows neural network models to generalize significantly better to unseen scenes. Talk Hongcheng Wang - Senior Manager, Technical R&D, Comcast Applied AI Research
DC8119 - Deep Learning for Streamlined Image Interpretation in Cancer and Biomedical Research

This talk will explain how high quality image segmentation is critical in biomedical image interpretation for accurate diagnosis and/or assessment of a disease. The FNLCR IVG aims to integrate deep learning into image analysis workflows to produce quantitative, accurate, high throughput, and reproducible results to streamline image interpretation. We trained CNNs for mice tumor segmentation on MRI images for radiomics studies on patient derived xenograft (PDX) models. We trained CNNs and developed software infrastructures for feature quantification of whole slide histology images applied to collagen network analysis and stroma segmentation. Key features include the ability to annotate whole slides, incorporate multiplexed features, and providing an interactive interface for "human-in-the-loop" review and feedback.

Talk Yanling Liu - Manager, FNLCR/Leidos Biomedical Research
DC8168 - Deep Learning Implementers Speak: Insights for Deep Learning Performance, Productivity and Scale This talks brings together A.I. implementers who have deployed deep learning at scale using NVIDIA DGX Systems. We'll focus on specific technical challenges they faced, solution design considerations, and best practices learned from implementing deep learning platforms. Attendees will gain insights such as: 1) how to set up your deep learning project for success by matching the right hardware and software platform options to your use case and operational needs; 2) how to design your architecture to overcome unnecessary bottlenecks that inhibit scalable training performance; and 3) how to build an end-to-end deep learning workflow that enables productive experimentation, training at scale, and model refinement. Talk Tony Paikeday - Director, Product Marketing, NVIDIA
DC8161 - Developing and Deploying AI in Risk Averse Industries Learn how we overcame the odds of certifying computer vision and AI systems in an industry as risk adverse as the air traffic control sector. We use off-the-shelf cameras deployed in an airport environment to provide an out the window view of the airfield, create an enriched augmented reality view for better situational awareness, contingency and redundancy. In this talk, we take you through the steps from developing an AI using Nvidia frameworks, to deploying a camera system at an airport for air traffic control use as an imaging system as well as a tracking system using AI technology such as artificial neural networks. All the way through user acceptance tests and certification. This talk is intended as a lessons learned for your next project in smart cities or aerospace. The main focus of this talk lays on the tools used to develop AI and the tools used to understand and visualize neural networks. Talk Christian Thurow - Head of R&D, Searidge Technologies
DC8121 - Fighting the Opioid Crisis through Extreme Analytics In his talk, Todd will demonstrate how analytics at extreme speed and scale can help data scientists and analysts rapidly extract fresh insights from open and publicly available datasets related to the opioid health crisis. See how millisecond response time enables rapid and iterative geospatial exploration of this data, to help drive faster action and informed decision-making. The government produces the most socially-impactful and politically-powerful data in the world. New technologies have overcome traditional challenges of making this data both available and consumable, to help improve citizen services. For government agencies leveraging GPUs, there are new opportunities to analyze and visually interact with massive datasets without frustrating lag times. Talk Todd Mostak - CEO & Co-Founder, MapD Technologies, Inc.
W219632 - Fundamentals of Accelerated Computing with CUDA C/C++

Pre-Requisites: Basic C/C++ competency

The CUDA computing platform enables the acceleration of CPU-only applications to run on the world's fastest massively parallel GPUs. Experience C/C++ application acceleration by:

  • Accelerating CPU-only applications to run their latent parallelism on GPUs
  • Utilizing essential CUDA memory management techniques to optimize accelerated applications
  • Exposing accelerated application potential for concurrency and exploiting it with CUDA streams
  • Leveraging command line and visual profiling to guide and check your work

Upon completion of this workshop, you'll be able to accelerate and optimize existing C/C++ CPU-only applications using the most essential CUDA tools and techniques. You’ll understand an iterative style of CUDA development that will allow you to ship accelerated applications fast.

Pre-GTC DLI Workshops
W219629 - Fundamentals of Deep Learning for Computer Vision

Pre-Requisites: Basic technical background

Explore the fundamentals of deep learning by training neural networks and using results to improve performance and capabilities.

In this hands-on course, you’ll learn the basics of deep learning by training and deploying neural networks. You’ll learn how to:

  • Implement common deep learning workflows, such as image classification and object detection.
  • Experiment with data, training parameters, network structure, and other strategies to increase performance and capability.
  • Deploy your neural networks to start solving real-world problems.

Upon completion, you’ll be able to start solving problems on your own with deep learning.

Pre-GTC DLI Workshops
DC8104 - GPU Accelerated Data Science TBA Talk Joshua Patterson - Director of Applied Solutions Engineering, NVIDIA
DC8130 - GPU Coder: Integrating MATLAB with TensorRT Learn how GPU Coder produces high-performance CUDA code that harness the power of TensorRT automatically from a high-level algorithm description in MATLAB. Write your deep learning application with the expressive power of MATLAB, which enables you to performance inference from trained deep learning networks together with data augmentation and post-processing of the results to create a complete deployment-ready application. GPU Coder then generates optimized inference code for the whole application. The deep learning inference model is compiled down to TensorRT while the rest of the application logic is parallelized through creation of CUDA kernels and integration with CUDA optimized libraries like cuBLAS, cuFFT, etc. The generated code can be cross-compiled to any NVIDIA GPU device that supports TensorRT. This allows engineers and scientists to unlock the expressive ease-of-use of the MATLAB programming language while unleashing deep learning performance by leveraging TensorRT. Talk Jaya Shankar - Principal Developer, MathWorks
Girish Venkataramani - Sr Development Manager, MathWorks
DC8160 - GPUs and AI in Agriculture: Optimize Every Plant, Dramatically Reduce Chemical Usage This presentation will provide an overview of Blue River Technology's use of GPUs in developing their See and Spray technology for Precision Agriculture. We will motivate the use of Deep Learning in detection and classification of crops and weeds in production environments, and highlight the ways in which NVIDIA GPUs have provided the tools and platform for training powerful models. NVIDIA GPUs have also helped us perform real-time inference on working machines in the field. This talk will show how these systems perform and provide videos of the machines in operation. Talk Jim Ostrowski - VP, Engineering, Blue River Technology (John Deere)
DC8150 - How Synthetic Data can Revolutionize AI for Healthcare? We demonstrate that labeled synthetic medical images can be used to train deep networks for accurate cancer diagnostics, specifically in applications where annotated data is limited due to privacy concerns, underrepresentation of rare conditions, limited availability of experts, etc. Deep networks trained on limited data also suffer from the cross patient network adaptability problem where networks trained on one patient often fail to generalize to other patients. We show that by using synthetically generated medical images, we can train accurate deep networks for cancer diagnostics in two different medical imaging applications: a) polyp detection and classification in endoscopy for colorectal cancer detection b) breast cancer grading of histopathology images. Talk Faisal Mahmood - Postdoctoral Fellow, Johns Hopkins University
DC8153 - Large-scale AI deployments with Kubernetes on NVIDIA GPUs In this session, we'll explore some of the common challenges with scaling-out deep learning training and inference deployment on data centers and public cloud using Kubernetes on NVIDIA GPUs. Through examples, we'll review a typical workflow for AI deployments on Kubernetes. We'll discuss advanced deployment options such as deploying to heterogenous GPU clusters, specifying GPU memory requirements, and analyzing and monitoring GPU utilizations using NVIDIA DCGM, Prometheus and Grafana. Talk Shashank Prasanna - Product Marketing Manager, NVIDIA
DC8116 - Machine Learning for Wireless Spectrum Awareness In the DARPA RFMLS program, Expedition Technology, Inc. (EXP) is creating new or adapt existing, ML practices, structures, and algorithms specialized for the radiofrequency (RF) domain. Four key capabilities form the foundation for the eventual RFML System as well the overall structure of the RFMLS program: Feature Learning, Attention & Saliency, Autonomous RF Sensor Configuration, and Waveform Synthesis. EXP is applying these RF ML capabilities under RFMLS for RF Forensics (RF Feature Learning and RF Waveform Synthesis) and for RF Spectrum Awareness and Autonomous RF sensor configuration. This presentation will describe the initial progress in this ongoing program, describing the RF training dataset, deep learning architecture, and computationally demanding aspects of RF deep learning. Talk Greg Harrison - CTO, Expedition Technology, Inc.
Enrico Matei - Research Engineer, Expedition Technology, Inc.
DC8120 - Next-Gen NVMe-native Distributed File System Keeps the DGX Saturated with Data to Power AI for Autonomous Vehicles In this session, you will learn why NAS and legacy file systems cannot sufficiently support deep learning workloads—they leave GPUs idling and waiting for data. We will explain why implementing an NVMe-native next-generation file system, like WekaIO Matrix™, into the AI architecture delivers the low latency, high throughput capabilities necessary to keep a system like the DGX2 saturated with data. We will profile two autonomous vehicles use cases that found Matrix to be more performant and scalable than NAS or an AFA. We will also present real life TensorFlow benchmarks comparing WekaIO performance to a local SSD file system showing that Matrix is the only coherent shared storage that is even faster than the current caching solutions. It also allows customers to linearly scale performance by adding more GPU servers. Talk Liran Zvibel - Co-founder and CEO, WekaIO
DC8127 - Object Tracking and Track Analytics from Overhead Video with Deep Learning We will present on how Expedition Technology has developed a set of deep learning algorithms used for change detection, tracking, and track analytics, including anomaly detection and activity recognition from overhead imagery. Our trained 3-frame change detection algorithm utilizes a convolutional network whose outputs serve as seed points for an object tracker that robustly handles non-trivial motions (e.g. move-stop-move), object obscuration, and low SNR scenarios without requiring background subtraction. The tracker utilizes a Siamese network-based deep learning architecture and learns features of the object of interest to strengthen track consistency through obscuration and over long tracks. Talk Ryan Crawford - Senior Engineer, Expedition Technology
DC8105 - Parallel Implementation of Resampling Techniques for Particle Filtering on GPUs We'll discuss parallel implementations to resampling techniques, commonly used in particle filtering, and their performance on NVIDIA GPUs, including the embedded TX2. A novel parallel approach to implementing systematic and stratified schemes is the highlight, but we'll also feature an optimized version of the Metropolis resampling technique. There are two main challenges that have been addressed: Traditional systematic and stratified techniques are serial by nature, but our approach breaks the algorithm up in a way to allow implementation on a GPU while producing identical results to the serial method. Secondly, while the Metropolis method is well suited for a GPU, its naive implementation does not utilize coalesced accesses to global memory. Talk Matthew Nicely - Electrical Engineer, AMRDEC
DC8134 - Real-Time Sensor Fusion For Intelligent Video Analytics In this presentation, we will introduce the concepts and framework for KickView's real-time multi-sensor fusion and analytics. We will also present techniques and concepts for creating AI workflows for low-latency processing of video streams common in a range of applications. We will also present a new paradigm for situational awareness across multiple sensors that also enables passive spatial tracking. Algorithms for object detection, tracking, and behavior analytics will be introduced and discussed. Examples will be highlighted through use cases and real-world applications. Talk David Ohm - CEO and Co-Founder, KickView
DC8114 - Resisting Adversarial Attacks on Machine Learning Malware Detectors We will present a simple technique showing how to make these specific machine learning models immune to white-box gradient based attacks at a small cost to accuracy. This makes our solution robust to numerous attacks, and could benefit other domains with binary targeted attacks. Deep Learning is becoming a popular component to building malware detection systems. This is a domain where we need to be particularly concerned with adversarial attacks, which can be done easily today. We have looked at machine learning approaches which require no domain knowledge to train, such as MalConv, and their susceptibility to adversarial attack. Through empirical evaluation, we have found that this machine learning based solution can be more robust to black-box attacks that defeat anti-virus engines today. Talk William Fleshman - Senior Cyberspace Capabilities Engineer, U.S. Army
Jared Sylvester - Lead Scientist, Booz Allen Hamilton
DC8165 - Safety by Way of Supercompute The computational complexity of solving for vehicle autonomy is dramatically increased by safety requirements. We will present key tenants of solving for safety via supercompute, including redundancy, diversity, and AI. Talk Neda Cvijetic - Senior Manager, Autonomous Vehicles, NVIDIA
DC8103 - Simplifying AI for Communications, Radar, and Wireless Systems Deep learning continues to show benefit in significant aspects of sensor systems including computer vision, speech recognition, and cybersecurity. In parallel, radio frequency (RF) systems have become increasingly complex and the number of connected devices will significantly increase as IoT and 5G systems become prevalent. Deep learning within RF systems is a new field of research that shows promise for dealing with a congested spectrum, brining reliability enhancements, and simplifying the ability to build effective signal processing systems. The utilization of deep learning algorithms within RF technology has shown superior results and the ability to classify signals well below the noise floor when compared to traditional signal processing methods. Working with strategic partners, we have designed a software configurable wide-band RF transceiver system capable of performing real-time signal processing and deep learning with an NVIDIA Jetson TX2. We discuss RF specific system performance, collection of RF training data, and the software used by the community to create custom applications. Additionally, we will present data demonstrating applications in the field of deep learning enabled RF systems. Talk John Ferguson - CEO, Deepwave Digital
DC8133 - Tackling the Realities of Virtual Reality As a new computing paradigm, Virtual Reality (VR) is changing workflows and redefining how we interact with computers. Deep Learning (DL) is revolutionizing business processes, defining how autonomous machines interact with us and with the world, and demanding application developers learn new ways of working in every field touching compute. In this panel we explore the intersection of these two revolutions with VR industry innovators who are leveraging deep learning using NVIDIA GPU compute systems to bring depth to the VR experience. This discussion will focus on the use of Artificial Intelligence (AI) in both building rich VR environments and enhancing the user's interaction with the VR environment. Panelists will share their vision on how AI will shape the near future of VR and give the audience a view of potential challenges to that future. In this session, we will explore: o Pain points in creating VR experiences, which are driving adoption of AI in the VR space o Challenges encountered in using DL to bring rich content to life in a VR environment o Challenges of implementing DL-enhanced VR environment interaction within the latency-critical VR space o How DL/AI will continue to fundamentally change the VR space Talk David Luebke - Vice President, Graphics Research, NVIDIA
DC8147 - The Journey from a Small Development Lab Environment to a Production Datacentre for Deep Learning Applications In this session, you`ll do a dive deep into best practices and real world examples of leveraging the power and flexibility of local GPU workstations, such has the DGX Station, to rapidly develop and prototype deep learning applications. We'll demonstrate the setup of our small lab, which is capable of supporting a team of several developers/researchers, and our journey as we moved from lab to data center. Specifically, we'll walk through our experience, as a practical example, of building the TensorRT Inference Demo, aka Flowers, used by Jensen to demonstrate the value of GPU computing throughout the world-wide GTCs, including support for Kubernetes on NVIDIA GPUs. Talk Markus Weber - Senior Product Manager, NVIDIA
Ryan Olson - Solutions Architect, NVIDIA
DC8144 - The Keys to Deploying Self-Driving Cars Autonomous vehicles can drastically increase the safety of U.S. roads, where federal officials estimate 94% of all traffic accidents are caused by human error. In addition to road safety, AVs can also improve the quality of life, reducing congestion and freeing up valuable time spent in the car. To realize these significant benefits, manufacturers must develop this technology safely and comprehensively. This session will explore how breakthrough technologies like virtual reality simulation and deep learning are helping to accelerate safe development and deployment of AVs. It will also address, how are manufacturers and regulators can work to ensure the rules of the road evolve with autonomous driving technology, and the further benefits that driverless vehicles bring to our everyday lives. Panel Finch Fulton - Deputy Assistant Secretary for Transportation Policy, U.S. Department of Transportation
Bert Kaufman - Head, Corporate and Regulatory Affairs, Zoox
Brad Stertz - Director, Audi Government Affairs, Audi
DC8138 - Training and Inferencing DL Models with NVIDIA GPU and MapR Kubernetes Volume Plugin The audience will learn examples and common practices for using Kubernetes to leverage NVIDIA GPU computing power when building DL models. We use a converged data platform to serve as data infrastructure, providing distributed file system and key-value storage and streams. Kubernetes is an orchestration layer that manages containers to scale out the training and deployment of DL models using heterogeneous GPU clusters. We also leverage the ability to publish and subscribe to streams on the platform to build next-gen applications with DL models, and monitor the model performance and shift of feature distributions. Talk Dong Meng - Data Scientist, MapR Technologies
DC8171 - Training Mixed Precision Neural Networks with Tensor Cores: Theory and Practice Tensor Cores, introduced with the Volta GPU architecture, provide up to 125 teraflops of throughput for operations on IEEE half-precision floats. In the theory portion of this talk we will review the half-precision format, the features of Tensor Cores, and principles for building mixed precision neural networks in any framework. The practice portion will review these principles with examples in PyTorch and show how tools like Apex can automatically convert existing neural networks to use Tensor Cores. This conversion requires no change in model architecture or hyperparameters and has been successfully applied to visual, auditory, and linguistic tasks on multiple frameworks. Talk Christian Sarofeen - Senior Dev Tech Engineer, NVIDIA
DC8174 - Turbocharging the AI Pipeline with Python and Anaconda The rise of GPU-accelerated data science and AI has come about through a combination of open source innovation and better tooling to support reproducible workflows. However, as the diverse array of deep learning libraries continue to mature, attention is moving to other parts of the AI pipeline, including simulation, ETL, and deployment. In this talk, I'll review open source projects that address these other areas, such as Numba, for implementing custom simulations and data transformations on the GPU, and PyGDF, for GPU accelerated dataframes. I'll discuss how the Anaconda Distribution and its conda packaging system helps data scientists create reproducible environments and deploy models. Finally, I'll talk about how Anaconda Enterprise allows data science teams to collaborate efficiently on GPU-accelerated projects with each other, and supports AI workflows from data exploration all the way to deployment. Talk Stanley Seibert - Director of Community Innovation, Anaconda
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