GTC is a hub for AI research and innovation where government leaders, developers, researchers, engineers, and system integrators gather to discuss the latest in GPU computing. Discover how startups, federal agencies, and research institutions are leveraging high-performance computing—from data analytics to geospatial intelligence, to computer vision and deep learning. Experience breakthroughs in GPU-based supercomputing that enable deep learning for natural language processing, object detection, cyber threat detection, speech recognition, and much more.
Graph Database and Analytics in a GPU-Accelerated Cloud Offering
INTELLIGENT VOICE LIMITED
Deep Convolutional Neural Networks for Spoken Dialect Classification of Spectrogram Images Using DIGITS
SYSTAP, LLC Managing Partner
Blazegraph GPU provides 300X acceleration for SPARQL graph query and graph database management with acceleration for existing RDF/SPARQL and Property Graph (Tinkerpop) applications. Multi-GPU configurations can effectively manage billion+ edge graphs on single node machines with 4 or 8 K-80s. This is a cost-effective way to deliver high performance for graphs, but many end-users and applications do not have existing multi-GPU systems; current cloud offerings at this scale are not generally available. Cirrascale has developed a cloud-based solution for provisioning multi-GPU Tesla(TM) systems using its switch riser technology. This session details the Blazegraph GPU cloud offering on Cirrascale, demonstrate how to quickly deploy it in the cloud, and show graph benchmarks on cloud systems. Sign up for this session now >
ABOUT THE SPEAKER: Brad's passion is helping customers navigate complex technology and business challenges and delivering products and solutions that solve them quickly and effectively. He has focused on participating and running businesses that apply novel and advanced technology solutions to new mission and business problems. Over the course of his career, I have performed advanced technology development for commercial and government customers. His technology experience ranges from early work in modeling methodologies and knowledge representation dating back to precursors of DARPA's DAML program to more recent work with large scale data analytics using the Hadoop ecosystem, Accumulo, and related technologies. In his current role with SYSTAP, LLC, he is focused on leveraging products for high performance graph databases and analytics into business and mission areas.
GRAPHISTRY, INC., Founder
ABOUT THE SPEAKER: Leo Meyerovich co-founded Graphistry, Inc., in 2014 to scale visual graph analytics. Graphistry builds upon the founding team's work at UC Berkeley on the first parallel web browser and Superconductor, a declarative GPU-accelerated data visualization language. Leo's most referenced work is in language-based security: language design and automatic verification for web apps and across control. However, his broader work from the past 10 years has been in designing programming languages, receiving awards for his research on the first reactive web language (OOPSLA), automatic parallelization (PLDI), and sociological foundations (OOPSLA, SIGPLAN).
INTELLIGENT VOICE LIMITED, CTO
Deep convolution neural networks are designed for classification tasks involving static images. We'll outline the novel application of using such networks for speech processing tasks such as the identification of a speaker's dialect. Representing speech as spectrogram images, we'll show our recent results from the NIST language recognition competition, and discuss how the network training results can be improved by manipulation of the spectrogram images in a way appropriate in the context of speech applications. Sign up for this session now >
ABOUT THE SPEAKER: Nigel became a lawyer because his father told him to go out and get a proper job, for which he is now eternally grateful. He qualified as a solicitor in 1993, and has worked for some of the world's largest law firms and software companies. Nigel has been the CTO of Intelligent Voice, Speech Recognition Expert, for the last 7 years. As a keen technologist, Nigel is always on the lookout for new challenges, and so is often seen finding new ways of stretching existing techniques and technology. This has led to interesting commissions such as recovering data from an "unbreakable" portable recording device. He has also gained UK government recognition for high tech research in the form of a large grant to explore leading edge problems in speech research, such as ultra-high speed GPU accelerated speech recognition, and emotional analysis of telephone calls. Nigel contributes regularly to a number of publications, including the Huffington Post, and the Global Legal Post, and well as blogging on the Intelligent Voice website. He has featured in a number of newspaper articles, including the front page of the Wall Street Journal. The WSJ also made a video looking at the advanced techniques used by Intelligent Voice to track trader wrongdoing.
Senior Data Scientist
Multi-Source Fusion Using Deep Learning
Implementing Deep Learning for Video Analytics on Tegra X1
CARNEGIE MELON UNIVERSITY
Listen, Attend and Spell
DIGITAL GLOBE, Senior Data Scientist
DigitalGlobe's satellite constellation collects millions of square kilometers of earth's imagery daily, yielding high resolution data of our planet. By employing DL algorithms & NVIDIA GPUs, DigitalGlobe processes imagery & detect objects at speeds orders of magnitude faster than ever before. Emergency responders require a multitude of information sources to support their mission. DigitalGlobe utilizes several methodologies of fusing disparate data sets together. Social media, weather, other sensor types (eg. RADAR/LIDAR) & Satellite Imagery can be fused together to help decision makers answer questions. By combining the data sets based on their location and common categories from the DL algorithms, emergency responders & analysts are able to automatically verify objects on the ground. Sign up for this session now >
ABOUT THE SPEAKER: Andrew Jenkins works for DigitalGlobe Inc. as a Senior Data Scientist focused on applying deep learning to multi-source spatial data such as satellite imagery, geo-tagged photos and videos. Andrew is currently a PhD candidate in the Department of Geography and Geographic Information Science at George Mason University. He holds a MS degree in Geoinformatics and a BS in Computer and Information Science. Andrew previously worked as a government researcher at the US Army Engineer Research and Development Center, and prior to that spent eight years in the military.
HERTA SECURITY, CEO
Implementing Deep Learning for Video Analytics on Tegra X1
The performance of Tegra X1 architecture opens the door to real-time evaluation and deployment of deep neural networks for video analytics applications. This session presents a highly-optimized low-latency pipeline to accelerate demographics estimation based on deep neural networks in videos. The proposed techniques leverage the on-die hardware video decoding engine and Maxwell GPU cores for conducting advanced video analytics such as gender or age estimation. Our results show that Tegra X1 is the right platform for developing embedded video analytics solutions. Sign up for this session now >
ABOUT THE SPEAKER: Dr. Javier Rodriguez Saeta is CEO of Herta Security, which he founded in 2009. He received M.S. and Ph.D. in electrical engineering from the Universitat Politecnica de Catalunya in 2000 and 2005, respectively. He received a B.A. in business administration from the Open University of Catalonia, and an MBA from ESADE Business School. In 2000, he worked for Robert Bosch, GmbH, in Hildesheim, Germany. In 2001, he joined Biometric Technologies, in Barcelona, Spain, where he was the R&D manager. He has published more than 20 papers indifferent magazines and workshops, and holds three patents. His main research interests include all issues related to innovation, security, and biometric systems and applications.
CARNEGIE MELON UNIVERSITY, Ph.D. Candidate
Most recently, Listen, Attend and Spell (LAS) was presented to directly transcribe speech utterances to characters. Unlike traditional DNN-HMM models, these models learn all the components of a speech recognizer jointly. The LAS model has two components: a listener and a speller. The listener is a pyramidal recurrent network encoder that accepts filter bank spectra as inputs. The speller is an attention-based recurrent network decoder that emits characters as outputs. The network produces character sequences without making any independence assumptions between the characters. In this talk, we describe a distributed asynchronous training platform for training such an model on an array of GPUs. Sign up for this session now >
ABOUT THE SPEAKER: William Chan is a Ph.D. candidate at Carnegie Mellon University in the Department of Electrical and Computer Engineering. William graduated with an M.S. in electrical and computer engineering from Carnegie Mellon University in 2013, and a B.S. in computer engineering in 2011 from the University of Waterloo. His past industry experience includes internships at Google, Amazon, Intel, NVIDIA, AMD, and TD Securities. His current research crosses the fields of machine learning, deep learning, and speech recognition.