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embedded world 2018

Evolving Embedded Systems in a Self-Directed World

Mark Papermaster AMD
Mark Papermaster, CTO, and SVP of Technology and Engineering, AMD
© AMD

The overall field of Machine Learning, including Machine Intelligence, is taking a fascinating, and not unexpected, direction: solving the world’s »big problems«.

How do we get more people where they want to go safely with autonomous driving? How do we increase the throughput, reliability and safety of our food supply chain with smarter devices? How can we make people healthier by analyzing medical data and relationships so complex that no human can reliably comprehend it? How do we better understand and improve our world with planet-scale data analysis? Machine Intelligence may not be able to address every problem but there are immediate areas where we can put it to use.

This is all just the tip of the iceberg. We see Machine Intelligence and Machine Learning used today as the latest tool humanity can use to improve efficiency and solve very challenging problems. There is just so much data out there today, generated by the plethora of sensors and IoT applications that pervade our offices and homes. Over the next few years, we’ll see Machine Learning help us better understand all this data, make it useful, and then ultimately act on it in new and exciting ways.

For many MI applications, the data must first be analyzed at the point and time of creation. Edge Computing is critical to provide real-time analysis, sifting and compressing data to make it useable, and then it must work in concert with even more global processing in the cloud. This key requirement to analyze massive data at the edge will be transformational to the embedded market. Today’s smart devices will increasingly need more and more high-performance.

AMD is focused on the compute engine aspects of Machine Learning. We are developing high performance compute engines and enabling CPU and GPU processors to support the evolving Machine Intelligence algorithm models, from the edge of the network and up into the cloud. To make application development efficient and more affordable, we are making software enablement open source to facilitate the community at large to speed application development.
We are inspired by Machine Learning and see an ever-increasing need for advancement. High performance GPUs and CPUs must evolve in sync with the rapid advance in Machine Learning technology. It is critical that these platforms provide both the performance and the efficiency for a wide range of applications. In my keynote “Evolving Embedded Systems in a Self-Directed World” this afternoon, I will discuss these trends and be joined on stage by customers pursuing Machine Learning, and they will share details on today’s issues and where they are headed in the future.

Mark Papermaster is CTO and SVP of Technology and Engineering with AMD.

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