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Unified Deep Learning Configurations and Emerging Applications

This is the final post in a five-part series from a report exploring the potential machine and a variety of computational approaches, including CPU, GPU and FGPA technologies. This article explores unified deep learning configurations and emerging applications. 

The Need for Deep Learning Transparency

Steve Conway from Hyperion Research gave this talk at the HPC User Forum. “We humans don’t fully understand how humans think. When it comes to deep learning, humans also don’t understand yet how computers think. That’s a big problem when we’re entrusting our lives to self-driving vehicles or to computers that diagnose serious diseases, or to computers installed to protect national security. We need to find a way to make these “black box” computers transparent.”

DNN Implementation, Optimization, and Challenges

This is the third in a five-part series that explores the potential of unified deep learning with CPU, GPU and FGPA technologies. This post explores DNN implementation, optimization and challenges. 

Exploring the Possibilities of Deep Learning Software

This is the second post in a five-part series from a report that explores the potential of unified deep learning with CPU, GPU and FGPA technologies. This post explores the possibilities and functions of software for deep learning.

DDN Powers Data Solutions for AI at the GPU Technology Conference

In this video from the GPU Technology Conference, James Coomer from DDN describes how the company delivers high performance data solutions for machine learning and AI applications. “DDN customers are leveraging machine learning techniques to speed results and improve competitiveness, profitability, customer service, business intelligence, and research effectiveness. The performance and flexible sizing of DDN solutions are well-suited for large-scale machine learning programs. They have the power to feed massive training sets to high core count systems as well as the mixed I/O capabilities necessary to handle data efficiently for CPU, GPU, and mixed multi-algorithm environments from simple linear regressions to deep neural nets.”

The Machine Learning Potential of a Combined Tech Approach

This is the first in a five-part series from a report exploring the potential of unified deep learning with CPU, GPU and FGPA technologies. This post explores the machine learning potential of taking a combined approach to these technologies. 

Video: Addressing Key Science Challenges with Adversarial Neural Networks

Wahid Bhimji from NERSC gave this talk at the 2018 HPC User Forum in Tucson. “Machine Learning and Deep Learning are increasingly used to analyze scientific data, in fields as diverse as neuroscience, climate science and particle physics. In this page you will find links to examples of scientific use cases using deep learning at NERSC, information about what deep learning packages are available at NERSC, and details of how to scale up your deep learning code on Cori to take advantage of the compute power available from Cori’s KNL nodes.”

Unified Deep Learning with CPU, GPU and FPGA Technologies

Deep learning and complex machine learning has quickly become one of the most important computationally intensive applications for a wide variety of fields. Download the new paper — from Advanced Micro Devices Inc. (AMD) and Xilinx Inc. — that explores the challenges of deep learning training and inference, and discusses the benefits of a comprehensive approach for combining CPU, GPU, FPGA technologies, along with the appropriate software frameworks in a unified deep learning architecture.

Video: The Convergence of HPC and Deep Learning

Axel Koehler from NVIDIA gave this talk at the Switzerland HPC Conference. “The technology originally developed for HPC has enabled deep learning, and deep learning is enabling many usages in science. Deep learning is also helping deliver real-time results with models that used to take days or months to simulate. The presentation will give an overview about the latest hard- and software developments for HPC and Deep Learning from NVIDIA and will show some examples that Deep Learning can be combined with traditional large scale simulations.”

Video: Demystifying Parallel and Distributed Deep Learning

Torsten Hoefler from (ETH) Zürich gave this talk at the 2018 Swiss HPC Conference. “Deep Neural Networks (DNNs) are becoming an important tool in modern computing applications. Accelerating their training is a major challenge and techniques range from distributed algorithms to low-level circuit design. In this talk, we describe the problem from a theoretical perspective, followed by approaches for its parallelization.”