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GTC DC 2017
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DC7243 - Accelerated Deep Learning Advances in HPC Recent advances in the deployment of deep learning recurrent nets have been demonstrated in scaling studies of Princeton's new Deep Learning Code -- "FRNN (Fusion Recurrent Neural Net) Code on modern GPU systems. This is a "big-data" project in that it has access to the huge EUROFUSION/JET disruption data base of over a half-petabyte to drive these studies. FRNN implements a distributed data parallel synchronous stochastic gradient approach with TensorFlow and Theano libraries at the backend and MPI for communication. This deep learning software has recently demonstrated excellent scaling up to 6,000 GPUs on Titan at Oak Ridge National Lab. The associated accomplishments exhibit clear progress toward the goal of establishing the practical feasibility of using leadership-class supercomputers to greatly enhance training of neural nets for transformational impact on key discovery science application domains such as fusion energy science. Powerful systems expected to be engaged for near-future deployment of this deep learning software include: (1) NVIDIA's SATURN V – featuring its nearly 1,000 Pascal P100 GPUs; (2) Switzerland's Piz Daint Cray XC50 system with 4,500 P100 GPUs; (3) Japan's Tsubame 3 system with 3,000 P100 GPUs; (4) and OLCF's Summit-Dev system. Summarily, deep learning software trained on large scientific datasets hold exciting promise for delivering much-needed predictive tools capable of accelerating knowledge discovery. The associated creative methods being developed – including a new half-precision capability -- also has significant potential for cross-cutting benefit to a number of important application areas in science and industry. 25 Minute Talk William Tang - Professor, Princeton University
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DC7111 - Accelerating Cyber Threat Detection with GPUs Analyzing vast amounts of enterprise cyber security data to find threats can be cumbersome. Cyber threat detection is also a continuous task, and because of financial pressure, companies have to find optimized solutions for this volume of data. We'll discuss the evolution of big data architectures used for cyber defense and how GPUs are allowing enterprises to efficiently improve threat detection. We'll discuss (1) briefly the evolution of traditional platforms to lambda architectures and ultimately GPU-accelerated solutions; (2) current GPU-accelerated database, analysis tools, and visualization technologies (such as MapD, BlazingDB, H2O.ai, Anaconda and Graphistry), and discuss the problems they solve; (3) the need to move beyond traditional rule based indicators of compromise and use a combination of machine learning, graph analytics, and deep learning to improve threat detection; and finally (4) our future plans to continue to advance GPU accelerated cyber security R&D as well as the GPU Open Analytics Initiative. 50 Minute Talk Josh Patterson - Director of Applied Solutions Engineering, NVIDIA
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DC7249 - Accelerating Your VR Applications with the VRWorks SDK Across graphics, audio, video, and physics, the NVIDIA VRWorks suite of technologies helps developers maximize performance and immersion for VR applications. We'll explore the latest features of VRWorks, explain the VR-specific challenges they address, and provide application-level tips and tricks to take full advantage of these features. Special focus will be given to the latest VRWorks integrations into Unreal Engine and Unity. 25 Minute Talk Victoria Rege - Global Alliances & Ecosystem Development, VR, NVIDIA
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DC7255 - Activating Tissue Data in the Era of Computational Medicine Pathology departments and translational research centers have amassed invaluable, untapped information in the form of glass pathology slides. The recent proliferation of digital pathology has shifted pathology into the era of computational medicine by creating the opportunity to quantify and integrate tissue data to supplement the existing cancer model centered around human expertise, and corresponding patient history and "-omic" data. With the help of clinical partners and massive computing infrastructures provided by NVIDIA, we are developing deep learning powered tools that activate those digital slides to address problems in the clinic and inform disease prognosis and therapeutic plans. This talk will outline our approach to deep learning based pathology image processing, including coarse feature embedding using fully convolutional networks and stain normalization using Generative Adversarial networks. It will also highlight a few of our recent successes in this domain including image classification applications for identifying metastases in breast and gastric lymph nodes with and image biomarker generation using deep learning to predict lymph node metastasis from the primary tumor histology. 50 Minute Talk Hunter Jackson - Chief Scientific Officer, Proscia Inc
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DC7200 - Adapting Deep Learning to New Data Using ORNL's Titan Supercomputer There has been a surge of success in using deep learning as it has provided a new state of the art for a variety of domains. While these models learn their parameters through data-driven methods, model selection through hyper-parameter choices remains a tedious and highly intuition-driven task. We've developed two approaches to address this problem. Multi-node evolutionary neural networks for deep learning (MENNDL) is an evolutionary approach to performing this search. MENNDL is capable of evolving not only the numeric hyper-parameters, but is also capable of evolving the arrangement of layers within the network. The second approach is implemented using Apache Spark at scale on Titan. The technique we present is an improvement over hyper-parameter sweeps because we don't require assumptions about independence of parameters and is more computationally feasible than grid-search. 25 Minute Talk Travis Johnston - Postdoctoral Research Associate, Oak Ridge National Laboratory
Steven Young - Research Scientist in Deep Learning, Oak Ridge National Laboratory
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DC7139 - Advanced Analytics and Machine Learning with Geospatial Data: A World of Possibilities As data sources such as sensors, social media, mobile, cloud, and machine logs become pervasive within the enterprise and the 4Vs of data managed by modern business applications increase exponentially, organizations need faster and better visualizations to explore data at scale and discover game-changing insights. Organizations are increasingly adopting modern technologies such as GPUs to interactively visualize billions of data elements in real-time and take faster business decisions. You'll learn how you can augment your existing business applications and visualization capabilities with GPU-accelerated rendering of maps and accompanying dashboards for spatial awareness and location-based analytics, and how GPU's massive parallelization minimizes the need for data preparation and sampling and delivers brute force capabilities to interactively visualize the fast-moving location and time-series data such as trading, traffic, social media, and vehicle telematics at scale and with speed. 25 Minute Talk Amit Vij - CEO, Kinetica
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DC7129 - 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. 25 Minute Talk Jesse Clayton - Senior Manager of Product Management for Intelligent Machines, NVIDIA
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DC7177 - AI, HPC Infrastructure, & Visualizations on Microsoft Azure Government Learn how Microsoft Azure Government enables compliant deep learning and traditional HPC-based workloads using powerful NVIDIA Tesla GPU accelerators and scale out using Azure's low-latency networking backed by InfiniBand infrastructure. Find out about the roadmap to support visualization scenarios and hear about customer stories and partner solutions that are leveraging these platform capabilities. 50 Minute Talk Kyle Deeds - Capacity and Specialized Hardware Lead, Azure Government Engineering, Microsoft
Zach Kramer - Engineer Lead, Azure Government, Microsoft
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DC7246 - AI in Healthcare: Beyond the Hype Cycle "AI is transforming healthcare" is the buzz around every news alert these days—but is it true? Where is AI and deep learning affecting healthcare and how is it impacting the medical imaging space? Join a thought leadership panel as government, industry and academic experts discuss the real calibration of the space—separating reality from the noise to explore how deep learning is advancing clinical practice, including advancements to overcome data and regulatory challenges. 50 Minute Panel Mike Tilkin - Chief Information Officer & Executive Vice President, American College of Radiology
Carla Leibowitz - Head of Strategy and Marketing, Arterys
Elliot Fishman - MD FACR Professor of Radiology, Surgery, Oncology and Urology, Johns Hopkins University
Katherine P. Andriole - Director of Research Strategy and Operations CCDS, Associate Professor of Radiology, Harvard Medical School, MGH & BWH Center for Clinical Data Science
Ronald Summers - Senior Investigator, NIH Clinical Center
Abdul Hamid Halabi - Global Business Development Lead, Healthcare & Life Sciences, NVIDIA
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DC7264 - AI Startup Showcase From intelligent machines to financial services to healthcare, AI startups are driving innovation across industries. NVIDIA Inception accelerates AI startups globally and recently accepted it's 2000th member.Founders from top startups in the program will take center stage at GTC DC in an interactive Inception Spotlight session to showcase their pioneering work. 50 Minute Panel Christine Haakenson - CEO, Parabricks
Marc Gyongyosi - CEO, IFM (Intelligent Flying Machine)
Eric Lee - CEO, DeepLearni.ng
James Crawford - Founder & CEO, Orbital Insight
Elliot English - CTO, Pilot AI Labs
Jonathan Su - CEO, Pilot AI Labs
Kimberly Powell - Senior Director of Deep Learning, NVIDIA
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DC7114 - AI: Transforming Your Work and the World Now Artificial intelligence is changing the world at an accelerating pace. AI has quickly jumped from research labs to business and consumer applications. In this keynote, Greg will share the latest developments in AI for transportation, robotics, manufacturing, healthcare and government. 60 Minute Keynote Greg Estes - Vice President Developer Programs, NVIDIA
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DC7260 - Alexa, Tell me how Kaldi and Deep Learning Revolutionized Automatic Speech Recognition We'll review the history of automatic speech recognition (ASR) technology, and show how deep neural networks have revolutionized the field within the last 5 years, giving birth to Alexa, enhancing Siri and nudging Google Home to market, and generally making ASR a household phenomenon. The story will be told from the viewpoint of Kaldi, a widely used set of open-source ASR tools in both academia and industry, touching on some key milestones and seminal developments along this short-yet-exciting journey, such as suitable network architectures, novel training criteria, and scalable optimization algorithms, along with prescient research funding, realistic data sets, and competitive benchmark tests conducted by neutral entities. 50 Minute Talk Sanjeev Khudanpur - Associate Professor of Electrical and Computer Engineering, The Johns Hopkins University
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DC7157 - A Look Inside the US Navy's Mixed Reality Lab Join us for a look inside the U.S. Navy's mixed reality lab, including an overview of Unity-based AR/VR projects to support the warfighter. We'll highlight both the recent live-fire exercise aboard the U.S.S. Bunker Hill, performed to test an augmented reality heads-up display as part of a topside ship gunnery system, and the Secretary of the Navy Innovation Award-winning work the team is doing to create 3D lidar scans of the entire U.S. Navy fleet and a collaboration environment that supports ship installations. 50 Minute Talk Joshua Harguess - Research Scientist, Gunnery System with Augmented Reality (GunnAR) Lea, SPAWAR Systems Center, Pacific
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DC7216 - A Machine Learning Approach for Tissue Biomechanics We're developing a machine learning strategy for improving lung and liver cancer treatments. Specifically, near-real-time lung and liver tissue elasticity estimations using only deformation maps that would typically be available from 4DCT lung images. The key technical innovation presented by our work is the integration of patient-specific, GPU-based biomechanical models with GPU-based machine learning approaches. For learning purposes, we employed TensorFlow. Enabling such measurements within the radiotherapy setup facilitates multiple advancements, including: (a) functional lung and liver preserving radiotherapy treatment planning, where the patient's treatment efficacy and quality of life can be significantly improved, (b) patient treatment response monitoring, where the impact of treatment on the patient can be monitored, and (c) novel therapeutic approaches, where the treatment can be adapted to the patient's disease conditions. 25 Minute Talk Anand Santhanam - Associate Professor, University of California, Los Angeles.
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DC7227 - An Overview of AI on Amazon Web Services Amazon Web Services offers a family of intelligent services that provide cloud-native machine learning and deep learning technologies to address different use cases and needs. For developers looking to add managed AI services to their applications, AWS brings natural language understanding and automatic speech recognition with Amazon Lex, visual search and image recognition with Amazon Rekognition, text-to-speech with Amazon Polly, and developer-focused machine learning with Amazon Machine Learning. The AWS Deep Learning Amazon Machine Images provides a way for AI developers and researchers to quickly and easily begin using any of the major deep learning frameworks (Apache MXNet, TensorFlow, Caffe, and others) to train sophisticated, custom AI models; experiment with new algorithms; and learn new DL skills and techniques on AWS' massive GPU-accelerated compute infrastructure. We'll provide a meaningful overview of how to improve scale and efficiency of AI applications on the AWS Cloud. 50 Minute Talk Vikram Madan - Senior Product Manager, AWS Deep Learning, Amazon Web Services
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DC7258 - Applying Neural Networks to Enhance Drug Discovery Berkeley Lights has developed unique capabilities to select, sort and annotate single live cells using light with a nanofluidic chip.  By combining this capability with deep learning, Convolutional Neural Networks and Nvidia GPUs, BLI can accurately identify, characterize and assay cells.  Applying this to antibody discovery for novel therapeutics, the Beacon platform automatically identifies the rare cells out of thousands that are producing the antibody of interest. This can reduce timelines from months to less than a week allowing scientist to iterate significantly faster. 25 Minute Talk John Tenney - Director of Algorithms, Berkeley Lights
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DC7171 - Applying Virtual Reality, and Augmented Reality to the Lifecycle Phases of Complex Products We'll demonstrate how to best utilize GPU technology for virtual reality and augmented reality applications. The lifecycle phases of a product such as a naval ship include design, production, deployment, operations, maintenance, and upgrades. Early in the design phase, it's typical to have the product modeled in a 3D CAD system. By adding physical properties to the CAD model, a virtual model can be created and used to perform physics-based simulations. These simulations are used to verify and validate the design. This virtual product model can be presented and manipulated in VR. The goal is to catch problems during the conceptual design phase before construction begins. VR and AR can both be applied during construction, operations, maintenance, and product upgrades. The Lockheed Martin Surface Navy Innovation Center (LM-SNIC) is investigating the best practices for applying VR and AR to the lifecycle phases of complex products, such as naval ships, aircraft, and ground radars. 50 Minute Talk Christopher Crouch - Associated Member of Engineering Staff, Lockheed Martin
Rich Rabbitz - Principal Member of Engineering Staff, Lockheed Martin
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DC7161 - A Sparse Dynamic Graph and Matrix Data Structure Sparse data computations are ubiquitous in science and engineering. Two widely used applications requiring sparse data computations are graph algorithms and linear algebra operations such as sparse matrix-vector multiplication (SpMV). In contrast to their dense data counterparts, sparse-data computations have less locality and more irregularity in their execution -- making them significantly more challenging to optimize. While there are several existing formats for sparse data representations, most of these are restricted to static datasets. We'll show a new data structure called Hornet for dynamic graphs and matrices that scales to extremely large graphs. Hornet can be used for both static and dynamic graph-based problems. 25 Minute Talk Oded Green - Research Scientist, Georgia Tech
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DC7170 - Automated Cotton Weeding We'll present how our see-and-spray machines are being deployed in cotton fields across the southern U.S. to tackle a problem farmers are fighting every day: weed pressure. 25 Minute Talk Lee Redden - CTO, Blue River Technology
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DC7143 - Autonomous Capabilities of the Joint Tactical Aerial Resupply Vehicle We'll discuss the latest updates and features to the autonomous precision landing and GPS-denied navigation capabilities of the Joint Tactical Aerial Resupply Vehicle (JTARV) platform. These capabilities are enabled by our high-performance computer vision libraries, Sentinel and HawkEye, both of which capitalize on NVIDIA's mobile GPUs and optimized deep learning frameworks. Autonomous navigation for aerial vehicles demand that core algorithms provide not only relevant, actionable information, but that they do so in a timely manner -- that is, the algorithms must operate in real time. We'll discuss how Sentinel object detection networks limit processing requirements for the autonomous precision landing capability. The requirement for high performance dictates optimization at every level, which is the focus of our ongoing research and development efforts. 25 Minute Talk Shawn Recker - Research Scientist, Survice
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DC7267 - Breaking New Frontiers in Robotics and Edge Computing with AI We'll cover the latest tools and techniques to deploy advanced AI at the edge. Get up to speed on recent developments in robotics and deep learning. 25 Minute Talk Dustin Franklin - GPGPU Developer and Systems Architect, NVIDIA
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DC7218 - Bringing DeepLearni.ng to the Enterprise: Applications of AI in Finance and Beyond Data-rich enterprise environments are gearing up for deep learning and artificial intelligence: these profound tools will transform how we tackle business opportunities or even revolutionize industries. But the journey of introducing AI to the enterprise is far from simple and is fraught with many challenges and unknowns. We'll systematically dissect technical and non-technical obstacles commonly faced when implementing and deploying AI at the enterprise. Presenting applications in finance and beyond, we'll discuss DeepLearni.ng's approach to overcome these obstacles in a comprehensive, results-driven manner. 25 Minute Talk Eric Lee - CEO, DeepLearni.ng
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DC7122 - Building the Foundation of America's AI-Enabled Cities Why does building AI Cities matter now for America? Why should the U.S. industry and government aggressively develop and deploy AI and deep learning to solve important problems around public safety and operational efficiency in our urban centers? What are the global trends that make this the right time to drive these changes? We'll cover these topics and more. 25 Minute Talk Adam Scraba - Global Business Development Lead, NVIDIA
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DC7235 - Caffe2: A New Lightweight, Modular, and Scalable Deep Learning Framework Caffe2 is a lightweight, modular, and scalable deep learning framework refactored from the previous Caffe. Caffe2 has been widely used at Facebook to enable new AI & AR experiences. We'll explain some framework basics, the strengths of Caffe2, large scale training support and will walk you through several product use-cases at Facebook including computer vision, machine translation, speech recognition and content ranking. We will also talk about how users benefit from Caffe2's built-in neural network model compression, fast convolution for mobile CPUs, and GPU acceleration. Also we will talk about the integration between ONNX and Caffe2. Open Neural Network Exchange (ONNX) is the first step toward an open ecosystem that empowers AI developers to choose the right tools as their project evolves. ONNX provides an open source format for AI models. It defines an extensible computation graph model, as well as definitions of built-in operators and standard data types. 50 Minute Talk Bram Wasti - Software Engineer, Facebook
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DC7251 - Classifying Structured Data with TensorFlow Estimators Estimators offer a high-level API with familiar methods such as train, evaluate, and predict. We'll introduce estimators and demonstrate how you can use them to train a deep neural network to classify structured data at scale. We'll also introduce feature engineering techniques, like bucketing, crossing, and embedding. 25 Minute Talk Josh Gordon - Developer Advocate, Google
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DC7140 - Codesigning Cognitive Computing Systems and Applications The development of cognitive computing applications is at a critical juncture with tough challenges but ample opportunities for great breakthroughs. Many of the cognitive solutions involved are complex and methods required to develop them remain poorly understood. Any major breakthrough in improving such understanding would require large-scale experimentation and extensive data-driven development. In short, we are witnessing the formation of a new modality of programming and even a new modality of application execution. The IBM-Illinois Center for Cognitive Computing Systems Research (C3SR) is developing scalable cognitive solutions that embody both advanced cognitive computing workloads and optimized heterogeneous computing systems for these cognitive workloads. The two streams of research not only complement, but also empower each other, and thus should be carried out in a tightly integrated fashion. 50 Minute Talk Wen-mei Hwu - Professor and Sanders-AMD Chair, University of Illinois at Urbana-Champaign
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SENB8AM - Coffee & Networking Join us for coffee & networking before the keynote. 120 Minute Special Event
DC7254 - Computational Pathology in Practice: From Cluster to Clinic Pathology is in the midst of a revolution from a qualitative to a quantitative discipline. This transformation is being fueled by machine and deep -learning. At Memorial Sloan Kettering we're building a computational pathology AI using an NVIDIA GPU cluster and a petabyte of clinical data. The goal is to transition the microscopic histological assessment of tissues from a manual and subjective process to a quantitative one. An AI that enables pathologists to efficiently and with speed, perform a pathological assessment that is more reproducible and objective. An AI that will facilitate large-scale, quantitative screening for correlations between tissue morphology and genetic panels like MSK-IMPACT. 25 Minute Talk Thomas Fuchs - Associate Professor, Memorial Sloan Kettering Cancer Center
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DC7115 - Connection Between Basic Research, AI and US Leadership Our nation's basic research programs, which are primarily funded by the government, have for decades produced amazing results for our country. Countless companies can point to fundamental technologies resulting from basic research they utilize that are the basis for their products or are the essential ingredient. Are we investing enough in basic research? What role should the U.S. government play in these areas? What is the promise of AI that needs more basic R&D to fulfill and how should we do it? 50 Minute Panel France Cordova - Director, National Science Foundation
Robie Samanta Roy - VP of Technology Strategy and Innovation, Lockheed Martin
Daniel Larson - Professor of Physics and Former Dean of the Eberly College of Science, Penn State
William Vanderlinde - Chief Scientist, IARPA
Fredrica Darema - Director, AFOSR, Air Force Office of Scientific Research
David Luebke - Vice President of Graphics Research, NVIDIA
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DC7153 - Convergence Among Disciplines and Technologies: AI, Science and Engineering Research, and HPC AI and machine learning techniques are finding increasing application across all areas of science and engineering, with high performance computing, data and networking playing a critical role in enabling research advances in these areas. In this talk we overview current and planned investments by the National Science in such advanced research cyberinfrastructure. We illustrate the use and promise of these technologies drawing on NSF-funded research at the intersection of a domain science, AI, and advanced research cyberinfrastructure. We'll also discuss how these investments are well aligned with NSF's forward-looking "Big Ideas." 50 Minute Talk James Kurose - Assistant Director, National Science Foundation
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DC7184 - Count-Ception: Counting by Fully Convolutional Redundant Counting Counting objects in digital images is a process that should be replaced by machines. This tedious task is both time-consuming and prone to errors due to fatigue of human annotators. The goal is to have a system that takes an image as input and returns a count of the objects inside and justification for the prediction in the form of object localization. We repose a problem, originally posed by Lempitsky and Zisserman, to instead predict a count map, which contains redundant counts based on the receptive field of a smaller regression network. We'll discuss a state-of-the-art method for counting objects in images and demonstrate its effectiveness on transmitted light microscopy images. 25 Minute Talk Joseph Paul Cohen - Postdoctoral Fellow , Montreal Institute for Learning Algorithms
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DC7214 - Counting Passenger Vehicles Using Satellite Imagery Learn the challenges surmounted in developing a vehicle counting algorithm from 30-centimeter satellite imagery. Counting cars from 30-cm resolution imagery is a much more challenging problem than counting cars from even higher resolution aerial (drone) imagery with only a few centimeters. And, since vehicles are composed of both metal and glass, and since light easily passes through glass, the vehicles displayed in satellite imagery are disjointed pixel blobs (that is, a mixture of dark and bright pixels). We'll discuss the challenges faced in developing a detection and counting deep learning algorithm that leverages NVIDIA's GPU capabilities, and how we arrived at a viable solution that is composed of fusing two convolutional neural networks for initially performing segmentation for vehicle detection followed by a more refined localization of the vehicles within each detected vehicle segment using morphological operations, non-maximum suppression, and connected components. 25 Minute Talk Kevin Green - Machine Learning and Computer Vision Scientist, DigitalGlobe Inc.
Mahmoud Lababidi - Senior Data Scientist, DigitalGlobe Inc.
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DC7127 - Cross-Domain Face Recognition Solution Based On GPU-Powered Deep Learning and Inference Government agencies and commercial companies demonstrate high demand for versatile, stable, and highly efficient person identification solutions supporting cross-domain face recognition and person database clusterization in both controlled and uncontrolled scenarios. Now it's possible to resolve cross-domain face recognition challenges using deep learning and even tasks of quadratic complexity using GPU-powered inference of CNN-based face recognition algorithms. We'll focus on (1) the concept of the GPU-powered platform for cross-domain face recognition; (2) its essential performance and critical technical characteristics; (3) an approach to reaching the demanded efficiency and quality by using the NVIDIA GPU; and (4) providing examples of completed and ongoing projects that demonstrate achieved high-performance and quality parameters in real-life conditions. 25 Minute Talk Alexander Khanin - CEO, VisionLabs
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DC7146 - CUDA 9 and Beyond CUDA is NVIDIA's parallel computing platform and programming model. In this talk, you'll learn about the new features in CUDA 9, including Cooperative Groups, an extension to the CUDA programming model for organizing groups of threads that helps you write efficient parallel algorithms that are safe, modular, and maintainable. You'll also learn about new features in CUDA that allow you to program the new Volta GPU architecture features such as Tensor Cores, providing the highest possible performance for deep learning training and inference. Finally, you'll get a preview of features and improvements coming to future releases of CUDA, and gain insight into the philosophy driving the development of CUDA and how it will take advantage of current and future GPUs. 50 Minute Talk Mark Harris - Chief Technologist, NVIDIA
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DC7112 - CUDA Optimization Tips, Tricks and Techniques Optimizing your code can be one of the most challenging tasks in GPU programming, but also one of the most rewarding: the performance difference between an initial version and well-tuned code can be a factor of 10 or more. Some optimizations can be quite straightforward while others require care and deep understanding of how the code is executing. A particular focus will be on optimization of the CPU part of your code, which is frequently overlooked even though it is often easier to tune and just as effective. Sometimes the biggest obstacle is just knowing what to look for, so we'll cover a range of techniques that everyone from beginners to CUDA ninjas might not have thought of before. 50 Minute Talk Stephen Jones - Principal Software Engineer, NVIDIA
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DC7128 - Current State of Autonomous Video Security at Enterprise Scale We'll explore how UMBO CV is leveraging deep learning techniques and GPUs to scale up how video is analyzed in real time and at enterprise scale. 50 Minute Talk Shawn Guan - CEO, Umbo CV Inc.
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DC7110 - Deep Learning Demystified What is deep learning? In what fields is it useful, and how does it relate to artificial intelligence? Join this session to get a working 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 superhuman accuracy, and the challenges organizations face in adopting this new approach. We'll also cover the software, hardware, and training resources that many organizations are using to overcome the challenges and deliver breakthrough results. Finally, we'll review several best practices for your first deep learning project. 50 Minute Talk Will Ramey - Director, Developer Programs, NVIDIA
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DC7172 - Deep Learning Deployment with NVIDIA TensorRT We'll explore some of the common challenges with deep learning deployment and how they can be addressed with NVIDIA TensorRT. Through an example, we'll review a typical workflow for taking a trained deep neural network to production to achieve desired throughput, latency, and energy-efficiency requirements. 50 Minute Talk Shashank Prasanna - Product Marketing Manager, NVIDIA
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DCL7102 - Deep Learning for Genomics using DragoNN with Keras and Theano In this lab, we use the dragonn toolkit on simulated and real regulatory genomic data, demystify popular DragoNN (Deep RegulAtory GenOmics Neural Network) architectures and provide guidelines for modeling and interpreting regulatory sequence using DragoNN models. We will answer questions such as: When is a DragoNN good choice for a learning problem in genomics? How does one design a high-performance model? And, more importantly, can we interpret these models to discover predictive genome sequence patterns to gain new biological insights? 120 Minutes Instructor-Led Lab Hoo Chang Shin - Certified Instructor, NVIDIA
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DC7186 - Deep Learning for Real Time Inventory Tracking We'll discuss how IFM uses deep learning for inventory tracking in logistics and manufacturing. 25 Minute Talk Marc Gyongyosi - CEO, IFM (Intelligent Flying Machine)
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DC7123 - Deep Learning for Real-time Threat Detection - From Active Shooters to Armed Robbery We'll explore how deep learning techniques can be used to transform passive surveillance systems into active threat-detection platforms for environments that range from retail, cities, and campuses. Deep Science is deploying deep learning solutions to spot robberies and assaults as they're occurring in real time. 25 Minute Talk Sean Huver - Founder and CEO, Deep Science
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DC7126 - Deep Learning in Medical Imaging-Applications in Ophthalmology, Radiology and Oncology Deep learning, facilitated by advances in hardware, software, and algorithms, has emerged as a leading technology in computer vision and image analysis and is being applied to medical imaging with early successes in radiology, oncology, ophthalmology, pathology, and others domains. We'll share our experiences addressing clinical questions in ophthalmology and radiology. In ophthalmology, we have created an automated system for classifying images for retinopathy of prematurity (ROP) using deep learning, while in radiology, we have developed tools for the cancer diagnosis and assessment of response to therapy. 50 Minute Talk Jayashree Kalpathy-Cramer - Director, QTIM lab, MGH/Harvard Medical School
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DC7137 - Deep Learning in Pulmonary Image Analysis with Incomplete Training Samples This session discusses the topic of computerized pulmonary image analysis from CT scans: the tasks, the unique challenges, and the solutions under deep learning framework. Specifically, we first provide the background of the problem, and limitations of conventional methods. Then, we list the major differences between natural and medical images, especially for pulmonary image analysis. Multiple common tasks will be covered including pathological lung segmentation, lobe fissure estimation, airway extraction, and disease pattern classification. For each candidate task, we then present its distinct features, which lead to a specific choice of deep learning structures. In addition, we discuss how to generate the training data that is usually constraint by limited clinical resources, and assess the design, applicability, performance, and generalizability of deep learning based methods. 25 Minute Talk Ziyue Xu - Staff Scientist, National Institutes of Health
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DC7217 - Deep Learning Object Detection and Segmentation This talk is a lightning introduction to object detection and image segmentation for data scientists, engineers, and technical professionals. This task of computer-based image understanding underpins many major fields such as autonomous driving, smart cities, healthcare, national defense, and robotics. Ultimately, the goals of this talk are to provide a broad context and clear roadmap from traditional computer vision techniques to the most recent state-of-the-art methods based on deep learning and convolution neural networks (CNNs). Additional considerations for network deployment at the edge or on the road in an autonomous vehicle using NVIDIA's latest TensorRT release will be discussed. 25 Minute Talk Abel Brown - Solutions Architect, NVIDIA
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DC7151 - Deep Learning with MATLAB: From Concept to Embedded Code Learn how to design a deep learning algorithm in MATLAB and deploy to an embedded Tegra platform, including Jetson TK1, TX1, TX2, and DRIVE PX boards. The workflow starts with algorithm design in MATLAB, which enjoys universal appeal among engineers and scientists because of its expressive power and ease of use. Algorithms used include deep learning augmented with traditional computer vision. Then, networks are trained using NVIDIA GPUs and parallel computing support in MATLAB either on the desktop, a local compute cluster, or in the cloud. Finally, a compiler auto-generates portable and optimized CUDA code from the MATLAB algorithm, which is then cross-compiled and deployed to the Tegra board. Generated code is highly optimized and we present benchmarks that show that performance of generated code is about two-and-a-half times faster than mxNet, about five times faster than Caffe2; about seven times faster than TensorFlow; and is on par with an optimized TensorRT implementation. 50 Minute Talk Ram Cherukuri - Product Marketing, MathWorks
Avinash Nehemiah - Product Manager- Computer Vision and Deep Learning , MathWorks
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DC7261 - Deep Learning with Quadro in Workstations Deep Learning with Quadro in workstations. Lenovo and NVIDIA partner to provide a wide range of hardware solutions for deep learning, for both training and inferencing applications, from the data center to the edge. This talk will focus on Quadro-based solutions for workstation environments. Specifically, Quadro GP100 in Lenovo ThinkStation provides an ideal solution with active cooling, high-bandwidth memory, and support for NVLink. 25 Minute Talk Scott Ruppert - Portfolio & Solutions Manager, Lenovo
Tim Woodard - Sr. Solutions Architect, NVIDIA
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DC7198 - DeepStream : Towards Large-scale Deployment of Intelligent Video Analytics Systems We'll introduce DeepStream, NVIDIA's solution for high-performance video analytics. One of the grand challenges of AI is to understand video content. Applications are endless: video surveillance, live video streaming, ad placement, and more. The problem is that deep learning, which has been boosting modern AI, is computationally expensive. It's even more challenging when it comes to live stream video. That's why we're building the NVIDIA DeepStream SDK, which simplifies development of high-performance video analytics applications powered by deep learning. It's built on top of the NVIDIA Video SDK, which can leverage the GPU's hardware encoding and decoding horsepower, and on top of NVIDIA TensorRT, which accelerates deep neural network's inferencing. We have seen successful large-scale deployment of such intelligent video analytics systems, and we see this as an unstoppable trend. 50 Minute Talk Jeremy Furtek - Senior Developer Technology Engineer, NVIDIA
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DC7144 - DGX System Deep-Dive: Best Practices for Scalable Deep Learning Performance NVIDIA DGX Systems powered by Volta deliver breakthrough performance for today's most popular deep learning frameworks. Attend this session and gain insights that will help researchers, developers, and data science practitioners accelerate training and iterate faster than ever. Learn (1) best practices for deploying an end-to-end deep learning practice, (2) how the newest DGX systems including DGX Station address the bottlenecks impacting your data science, and (3) how DGX software including optimized deep learning frameworks give your environment a performance advantage over GPU hardware alone. 25 Minute Talk Charlie Boyle - Senior Director, Product Management and Product Marketing, NVIDIA
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DC7108 - Disruptive Changes in Ophthalmology by Deep Learning Hear about how GPU technology is disrupting the way your eye doctor works and how ophthalmic research is performed today. The rise of electronic medical records in medicine has created mountains of big data, particularly in ophthalmology, where many discrete quantitive clinical elements like visual acuity can be tied to rich imaging datasets. We'll explore the transformative nature that GPU acceleration has played in accelerating clinical research and show real-life examples of deep learning applications to ophthalmology in creating new steps forward in automated diagnoses, image segmentation, and computer-aided diagnoses. 25 Minute Talk Aaron Lee - Assistant Professor, University of Washington
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DC7231 - Distributed GPU-Enabled TensorFlow Applications at Scale We'll dive deep into large-scale GPU-enabled distributed computing with TensorFlow on an on-premises cluster or the cloud. Since the introduction of distributed TensorFlow, many solutions have been used in an attempt to streamline the cumbersome process of application development and deployment. Having encountered these issues in our own GPU cluster, we present our system architecture and scheduling framework that addresses GPU allocation, containerization, deployment, and scheduling of GPU-enabled TensorFlow applications on a cluster. We will be demonstrating our current use cases that include large-scale distributed training of TensorFlow models and model hyperparameter optimization. 50 Minute Talk Keegan Hines - Machine Learning Engineer, Capital One Financial
Thuc Tran - Machine Learning Engineer, Capital One Financial
Athanassios Kintsakis - Machine Learning Engineer, Capital One Financial
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