Intel Chip Chat

With no GPS in space, how can a rover know its exact location on a lunar surface?

In this Chip Chap podcast, Phil Ludivig, rover navigation engineer with iSpace, Inc.* joins Shashi Jain, innovation manager at Intel, to talk about research that applied AI to one of the biggest challenges in space exploration.

Ludivig and Jain, along with other researchers, came together at NASA Frontier Development Lab (NASA FDL) to tackle questions facing NASA and the commercial space industry. Their team took on one of the most fundamental – and answered it a highly inventive way.

Starting with a game engine, the team created a simulated lunar environment to train an AI algorithm that produced the ground truth needed for machine learning. Next, they created synthetic images, called reprojections, from cameras mounted on a rover. AI matched reprojected images to actual orbital images, figuring out terrain features that made sense.

The team used Intel® AI DevCloud for inference, an Intel Core™ i7+ PC and Intel Xeon® Scalable processor-based server for synthetic training data generation, and Google* Cloud Platform for training.

The same technique can be applied anywhere, on Mars or even areas of Earth where GPS is out of reach.

For details about this and other NASA FDL projects, visit frontierdevlopmentlab.org.

Information about Phil Ludivig’s organization is online at ispace-inc.com.

More about AI at Intel is available at intel.com/ai.

Intel technologies' features and benefits depend on system configuration and may require enabled hardware, software or service activation. Performance varies depending on system configuration No product or component can be absolutely secure. Check with your system manufacturer or retailer or learn more at intel.com.

Intel, the Intel logo, Core, and Xeon are trademarks of Intel Corporation or its subsidiaries in the U.S. and/or other countries.

*Other names and brands may be claimed as the property of others.

© Intel Corporation

AI and HPC are highly complementary – flip sides of the same data- and compute-intensive coin.

In this Chip Chat podcast Dr. Pradeep Dubey, Intel Fellow and director of its Parallel Computing Lab, explains why it makes sense for the two technology areas to come together and how Intel is supporting their convergence.

AI developers tend to be data scientists, focused on deriving intelligence and insights from massive amounts of digital data, rather than typical HPC programmers with deep system programming skills.

Because Intel® architecture serves as the foundation for both AI and HPC workloads, Intel is uniquely positioned to drive their convergence. Its technologies and products span processing, memory, and networking at ever-increasing levels of power and scalability.

For more information on developing HPC and AI on Intel hardware and software, visit the Intel Developer Zone at software.intel.com.

More about AI activities across Intel is online at ai.intel.com. For details, click on the Technology and Research tabs.

Intel technologies' features and benefits depend on system configuration and may require enabled hardware, software or service activation. Performance varies depending on system configuration No product or component can be absolutely secure. Check with your system manufacturer or retailer or learn more at intel.com.

Intel and the Intel logo are trademarks of Intel Corporation or its subsidiaries in the U.S. and/or other countries.

*Other names and brands may be claimed as the property of others.

© Intel Corporation

In this Chip Chat podcast, Zahi Kakish, a doctoral student focusing on swarm robotics at Arizona State University, joins Shashi Jain, innovation manager at Intel, to talk about some remarkable work that’s emerged from NASA FDL, the space agency’s Frontier Development Lab.

Designed as an eight-week challenge, FDL is an AI accelerator project set up by NASA Ames, the SETI Institute, and private partners. Its goal? To bring together the best minds in AI and planetary science to tackle challenges facing NASA and the commercial space industry.

This past summer, with support from Intel principal engineers and an Intel® Xeon processor-based server at SETI, Kakish and his FDL team created a planning tool called the Mission Planner for Cooperative Multi-Agent Systems, MARMOT for short. Through the use of IA and machine learning, it enables two semi-autonomous rovers to communicate and work together to solve a task.

With its collaborative systems and assistive AI, MARMOT delivers a significant performance improvement over missions that employ a single robot with a human operator. It represents a fresh and highly optimized approach to autonomy, and it’s available as open source code.

To access the code for all NASA FDL projects, visit https://gitlab.com/frontierdevelopmentlab.

Additional information is available on the FDL site at https://frontierdevelopmentlab.org/.

To learn about the Autonomous Collective Systems Lab at Arizona State University and their work on swarm robotics, go to http://faculty.engineering.asu.edu/acs/.

More about Intel’s involvement in FDL is posted on https://intel.com/ai.

To access Intel AI DevCloud, a cloud hosted hardware and software platform that helps developers, researchers, and startups get started on AI projects, visit https://ai.intel.com/devcloud/.

Intel technologies' features and benefits depend on system configuration and may require enabled hardware, software or service activation. Performance varies depending on system configuration. No product or component can be absolutely secure. Check with your system manufacturer or retailer or learn more at intel.com.

Intel, the Intel logo, and Xeon® are trademarks of Intel Corporation or its subsidiaries in the U.S. and/or other countries.

*Other names and brands may be claimed as the property of others.

© Intel Corporation

In this Chip Chat podcast, Zahi Kakish, a doctoral student focusing on swarm robotics at Arizona State University, joins Shashi Jain, innovation manager at Intel, to talk about some remarkable work that’s emerged from NASA FDL, the space agency’s Frontier Development Lab.

Designed as an eight-week challenge, FDL is an AI accelerator project set up by NASA Ames, the SETI Institute, and private partners. Its goal? To bring together the best minds in AI and planetary science to tackle challenges facing NASA and the commercial space industry.

This past summer, with support from Intel principal engineers and an Intel® Xeon processor-based server at SETI, Kakish and his FDL team created a planning tool called the Mission Planner for Cooperative Multi-Agent Systems, MARMOT for short. Through the use of IA and machine learning, it enables two semi-autonomous rovers to communicate and work together to solve a task.

With its collaborative systems and assistive AI, MARMOT delivers a significant performance improvement over missions that employ a single robot with a human operator. It represents a fresh and highly optimized approach to autonomy, and it’s available as open source code.

To access the code for all NASA FDL projects, visit https://gitlab.com/frontierdevelopmentlab.

Additional information is available on the FDL site at https://frontierdevelopmentlab.org/.

To learn about the Autonomous Collective Systems Lab at Arizona State University and their work on swarm robotics, go to http://faculty.engineering.asu.edu/acs/.

More about Intel’s involvement in FDL is posted on https://intel.com/ai.

To access Intel AI DevCloud, a cloud hosted hardware and software platform that helps developers, researchers, and startups get started on AI projects, visit https://ai.intel.com/devcloud/.

Intel technologies' features and benefits depend on system configuration and may require enabled hardware, software or service activation. Performance varies depending on system configuration. No product or component can be absolutely secure. Check with your system manufacturer or retailer or learn more at intel.com.

Intel, the Intel logo, and Xeon® are trademarks of Intel Corporation or its subsidiaries in the U.S. and/or other countries.

*Other names and brands may be claimed as the property of others.

© Intel Corporation

In this Chip Chat podcast, Zahi Kakish, a doctoral student focusing on swarm robotics at Arizona State University, joins Shashi Jain, innovation manager at Intel, to talk about some remarkable work that’s emerged from NASA FDL, the space agency’s Frontier Development Lab.

Designed as an eight-week challenge, FDL is an AI accelerator project set up by NASA Ames, the SETI Institute, and private partners. Its goal? To bring together the best minds in AI and planetary science to tackle challenges facing NASA and the commercial space industry.

This past summer, with support from Intel principal engineers and an Intel® Xeon processor-based server at SETI, Kakish and his FDL team created a planning tool called the Mission Planner for Cooperative Multi-Agent Systems, MARMOT for short. Through the use of IA and machine learning, it enables two semi-autonomous rovers to communicate and work together to solve a task.

With its collaborative systems and assistive AI, MARMOT delivers a significant performance improvement over missions that employ a single robot with a human operator. It represents a fresh and highly optimized approach to autonomy, and it’s available as open source code.

To access the code for all NASA FDL projects, visit https://gitlab.com/frontierdevelopmentlab.

Additional information is available on the FDL site at https://frontierdevelopmentlab.org/.

To learn about the Autonomous Collective Systems Lab at Arizona State University and their work on swarm robotics, go to http://faculty.engineering.asu.edu/acs/.

More about Intel’s involvement in FDL is posted on https://intel.com/ai.

To access Intel AI DevCloud, a cloud hosted hardware and software platform that helps developers, researchers, and startups get started on AI projects, visit https://ai.intel.com/devcloud/.

Intel technologies' features and benefits depend on system configuration and may require enabled hardware, software or service activation. Performance varies depending on system configuration. No product or component can be absolutely secure. Check with your system manufacturer or retailer or learn more at intel.com.

Intel, the Intel logo, and Xeon® are trademarks of Intel Corporation or its subsidiaries in the U.S. and/or other countries.

*Other names and brands may be claimed as the property of others.

© Intel Corporation

Dr. Henry Jerez, Principal Group Product and Program Manager for Azure* Machine Learning Inferencing and Infrastructure at Microsoft, joins Chip Chat to discuss accelerating AI inference in Microsoft Azure. Dr. Jerez leads the team responsible for creating assets that help data scientists manage their AI models and deployments, both in the cloud and at the edge, and works closely with Intel to deliver the fastest-possible inference performance for Microsoft's customers. At Ignite 2018, Microsoft demoed an Azure Machine Learning model running atop the OpenVINO toolkit and Intel® architecture for highly-performant inference at the edge. This capability will soon be incorporated into Azure Machine Learning. Microsoft additionally announced at Ignite a refreshed public preview of Azure Machine Learning that now provides a unified platform and SDK for data scientists, IT professionals, and developers. For more on Microsoft Azure Machine Learning, please visit http://aka.ms/azureml-docs.

Intel technologies' features and benefits depend on system configuration and may require enabled hardware, software or service activation. Performance varies depending on system configuration. No product or component can be absolutely secure. Check with your system manufacturer or retailer or learn more at intel.com.

Intel and the Intel logo are trademarks of Intel Corporation or its subsidiaries in the U.S. and/or other countries.

*Other names and brands may be claimed as the property of others.

© Intel Corporation

Dr. Henry Jerez, Principal Group Product and Program Manager for Azure* Machine Learning Inferencing and Infrastructure at Microsoft, joins Chip Chat to discuss accelerating AI inference in Microsoft Azure. Dr. Jerez leads the team responsible for creating assets that help data scientists manage their AI models and deployments, both in the cloud and at the edge, and works closely with Intel to deliver the fastest-possible inference performance for Microsoft's customers. At Ignite 2018, Microsoft demoed an Azure Machine Learning model running atop the OpenVINO toolkit and Intel® architecture for highly-performant inference at the edge. This capability will soon be incorporated into Azure Machine Learning. Microsoft additionally announced at Ignite a refreshed public preview of Azure Machine Learning that now provides a unified platform and SDK for data scientists, IT professionals, and developers. For more on Microsoft Azure Machine Learning, please visit http://aka.ms/azureml-docs.

Intel technologies' features and benefits depend on system configuration and may require enabled hardware, software or service activation. Performance varies depending on system configuration. No product or component can be absolutely secure. Check with your system manufacturer or retailer or learn more at intel.com.

Intel and the Intel logo are trademarks of Intel Corporation or its subsidiaries in the U.S. and/or other countries.

*Other names and brands may be claimed as the property of others.

© Intel Corporation

Descartes Labs helps companies to get business insight from huge volumes of satellite and geographic data, using a combination of Software as a Service and custom development. Handling petabytes of data, compression is hugely important for packaging the data in usefully sized files and for driving down storage costs. By upgrading to the latest generation Intel® processor, provided in the Google Cloud Platform*, Descartes Labs was able to accelerate its compression.

To learn more about Descartes Labs solutions visit their website at https://www.descarteslabs.com/. To learn more about Intel's partnership with Google Cloud Platform visit https://cloud.google.com/intel/.

Intel technologies' features and benefits depend on system configuration and may require enabled hardware, software or service activation. Performance varies depending on system configuration No product or component can be absolutely secure. Check with your system manufacturer or retailer or learn more at intel.com.

Intel and the Intel logo are trademarks of Intel Corporation or its subsidiaries in the U.S. and/or other countries.

*Other names and brands may be claimed as the property of others.

© Intel Corporation

Dr. Ziya Ma, vice president of Intel® Software and Services Group and director of Data Analytics Technologies, gives Chip Chat listeners a look at data center optimization along with a preview of advancements well underway.

In their work with the broad industry, Dr. Ma and her team have found that taming the data deluge calls for IT data center managers to unify their big data analytics and AI workflows. As they’ve helped customers overcome the memory constraints involved in data caching, Apache Spark*, which supports the convergence of AI on big data, has proven to be a highly effective platform.

Dr. Ma and her team have already provided the community a steady stream of source code contributions and optimizations for Spark. In this interview she reveals that more – and even more exciting work – is underway.

Spark depends on memory to perform and scale. That means optimizing Spark for the revolutionary new Intel® Optane™ DC persistent memory offers performance improvement for the data center.

In one example, Dr. Ma describes benchmark testing where Spark SQL performs eight times faster at a 2.6TB data scale using Intel Optane DC persistent memory than a comparable system using DRAM DIMMs.

With Intel Optane DC persistent memory announced and broadly available in 2019, data centers have the chance to achieve workflow unification along with performance gains and system resilience starting now.

For more information about Intel’s work in this space, go to software.intel.com/ai.

For more about how Intel is driving advances in the ecosystem, visit intel.com/analytics.

Performance results are based on Intel internal testing: 8X faster insights (8/2/2018) based on Apache Spark* SQL IO intensive queries for Analytics vs. DRAM+HDD at 2.6TB data scale; 9X read transactions and 11X users per system (5/29/2018) based on Apache* Cassandra-4.0 workload doing 100% reads vs. comparable server system with DRAM & NAND NVME Drives; 12.5X faster restart times (5/30/2018) based on running SAP HANA 2.0 SPS 03, and may not reflect all publicly available security updates. No product can be absolutely secure. Configurations: Results have been estimated based on tests conducted on pre-production systems, and provided to you for informational purposes.

Software and workloads used in performance tests may have been optimized for performance only on Intel microprocessors. Performance tests, such as SYSmark and MobileMark, are measured using specific computer systems, components, software, operations and functions. Any change to any of those factors may cause the results to vary. You should consult other information and performance tests to assist you in fully evaluating your contemplated purchases, including the performance of that product when combined with other products. For more information go to www.intel.com/benchmarks.

Intel, the Intel logo, and Optane are trademarks of Intel Corporation or its subsidiaries in the U.S. and/or other countries.

*Other names and brands may be claimed as the property of others.

© Intel Corporation.

Christine McMonigal, Director of Hyperconverged Infrastructure at Intel, joins Chip Chat at the Microsoft Ignite 2018 conference to talk about what’s new from Microsoft and Intel.

The two companies have collaborated extensively on hyperconvergence. One result, announced at Ignite, is a refreshed version of Intel® Select Solutions for Windows Server* Software Defined Storage. It adds support for Microsoft’s newly announced Windows Server 2019, which in turn supports Intel® Optane™ DC persistent memory.

Intel is working with many industry partners, including Microsoft, to create what it calls Intel Select Solutions, full stack solutions optimized and benchmarked for verified performance. The aim is to bring more data centers into hyperconverged environments more readily.

To support Storage Spaces Direct, one of Windows Server 2019’s new features, Intel just released two configurations to the market as reference designs. The Base configuration can support a wide range of workloads while the Plus configuration is optimized for more latency-sensitive workloads like databases and analytics.

McMonigal gives us a look at what’s ahead, with Intel Optane DC persistent memory being used as a high-speed cache in hyperconverged solutions, to name just one exciting application.

For more information about Intel Select Solutions for Windows for Server Software Defined Storage, go to intel.com/selectsolutions.

For more about how Intel and Microsoft are partnering to accelerate business transformation, visit intel.com/microsoftdatacenter.

Software and workloads used in performance tests may have been optimized for performance only on Intel microprocessors. Performance tests, such as SYSmark and MobileMark, are measured using specific computer systems, components, software, operations and functions. Any change to any of those factors may cause the results to vary. You should consult other information and performance tests to assist you in fully evaluating your contemplated purchases, including the performance of that product when combined with other products. § For more information go to www.intel.com/benchmarks.

Performance results are based on testing as of 9/30/18 and may not reflect all publicly available security updates. See configuration disclosure for details. No product can be absolutely secure.

Intel technologies’ features and benefits depend on system configuration and may require enabled hardware, software or service activation. Performance varies depending on system configuration. Check with your system manufacturer or retailer or learn more at https://intel.com/.

92% savings by deduplication based on https://youtu.be/3wvTYbnXyB4.

Intel, the Intel logo and Optane are trademarks of Intel Corporation or its subsidiaries in the U.S. and/or other countries.

*Other names and brands may be claimed as the property of others.

© Intel Corporation