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The true cost of AI innovation

“As the world’s attention has shifted to climate change, the field of AI is beginning to take note of its carbon cost. Research done at the Allen Institute for AI by Roy Schwartz et al. raises the question of whether efficiency, alongside accuracy, should become an important factor in AI research, and suggests that AI scientists ought to deliberate if the massive computational power needed for expensive processing of models, colossal amounts of training data, or huge numbers of experiments is justified by the degree of improvement in accuracy.”

Fast Track your AI Workflows

In this special guest feature, our friends over at Inspur write that for new workloads that are highly compute intensive, accelerators are often required. Accelerators can speed up the computation and allow for AI and ML algorithms to be used in real time. Inspur is a leading supplier of solutions for HPC and AI/ML workloads.

The Role of Middleware in Optimizing Vector Processing

This whitepaper delves into the world of unstructured data and describes some of the technologies, especially vector processors and their optimization software, that play key roles in solving the problems that arise as result of the accelerating amount of data generated globally.

Podcast: Advancing Deep Learning with Custom-Built Accelerators

In this Chip Chat podcast, Carey Kloss from Intel outlines the architecture and potential of the Intel Nervana NNP-T. He gets into major issues like memory and how the architecture was designed to avoid problems like becoming memory-locked, how the accelerator supports existing software frameworks like PaddlePaddle and TensorFlow, and what the NNP-T means for customers who want to keep on eye on power usage and lower TCO.

One Stop Systems does AI on the Fly at SC19

In this video from SC19, Jaan Mannik from One Stop Systems describes how the company delivers AI on the Fly. “With AI on the Fly, OSS puts computing and storage resources for the entire AI workflow, not in the datacenter, but on the edge near the sources of data. Applications are emerging for this new AI paradigm in diverse areas including autonomous vehicles, predictive personalized medicine, battlefield command and control, and industrial automation.”

The Eco-System of AI and How to Use It

Glyn Bowden from HPE gave this talk at the UK HPC Conference. “This presentation walks through HPE’s current view on AI applications, where it is driving outcomes and innovation, and where the challenges lay. We look at the eco-system that sits around an AI project and look at ways this can impact the success of the endeavor.”

The Convergence of HPC and AI Workloads Requires Flexibility and Performance

Finding the best solution to meet the requirements for intertwined HPC and AI workloads requires us tolook at the overall platform benefits versus the benefits of individual technologies. With exascale on the horizon, the blending of HPC and AI algorithms, and ever-increasing data sets, having an overall robust platform is more important than ever. Intel makes the case for HPC and AI to share a common platform. 

Best Practices for Building, Deploying & Managing HPC Clusters

In today’s markets, a successful HPC cluster can be a formidable competitive advantage. And many are turning to these tools to stay competitive in the HPC market. That said, these systems are inherently very complex, and have to be built, deployed and managed properly to realize their full potential. A new report from Bright Computing explore best practices for HPC clusters. 

Five Essential Strategies for Successful HPC Clusters

Successful HPC clusters are powerful assets for an organization. However, these systems are complex and must be built and managed properly to realize their potential. If not done properly, your ability to meet implementation deadlines, quickly identify and resolve problems, perform updates and maintenance, accommodate new application requirements and adopt strategic new technologies will be jeopardized. Download the new white paper from Bright Computing that explores key strategies for HPC clusters.

AI Critical Measures: Time to Value and Insights

AI is a game changer for industries today but achieving AI success contains two critical factors to consider — time to value and time to insights.  Time to value is the metric that looks at the time it takes to realize the value of a product, solution or offering. Time to insight is a key measure for how long it takes to gain value from use of the product, solution or offering.