In an era of unprecedented demand for passenger and cargo aircraft, the aviation industry is focused on enhancing operations, reducing costs, ensuring reliability and safety, and achieving net-zero environmental goals -- and innovative solutions are needed to successfully accomplish this. Recent advancements in technologies like AI, ML, and computer vision powered by deep learning are revolutionizing the industry, enabling vision-based autonomous aircraft functions such as taxi, takeoff, and landing. These breakthroughs facilitate a paradigm shift from low-level aircraft control to high-level operational oversight, promising a new era of aviation efficiency. Acubed is at the forefront of this transformation, developing autonomous flight and AI solutions through rapid, data-driven software development. Leveraging large-scale data and compute infrastructure, machine learning, and simulation, our approach is meticulously guided by certifiable verification and validation to ensure safety and reliability.
Infrastructure
Hardware
Software
Systems
Data Center
Arne Stoschek
Vice President of AI, Autonomy & Digital Information
Acubed (Airbus)
Arne is the Vice President of AI, Autonomy & Digital Information and oversees the company’s development of autonomous flight and machine learning solutions to enable future, self-piloted aircraft. In his role, he also leads the advancement of large-scale data-driven processes to develop novel aircraft functions. He is passionate about robotics, autonomy and the impact these technologies will have on future mobility. After holding engineering leadership positions at global companies such as Volkswagen/Audi and Infineon, and at aspiring Silicon Valley startups, namely Lucid Motors/Atieva, Knightscope and Better Place, Arne dared to take his unique skill set to altitude above ground inside Airbus. Arne earned a Doctor of Philosophy in Electrical and Computer Engineering from the Technical University of Munich and held a computer vision and data analysis research position at Stanford University.
The LLM-based Generative AI revolution is progressing from the use of language-only models to multimodal models and the transition from monolithic models to more complex Agentic AI workflows. These workflows allow AI systems to address more complex tasks essential to enterprises by doing problem decomposition, planning, self-reflection, and tool use. This talk will share how Intel is collaborating with customers and developers to advance productivity and applications of AI to such higher cognitive tasks using Intel® Gaudi® 3 AI Accelerators, including massive AI cluster buildout in Intel® Tiber™ Developer Cloud.
Infrastructure
Hardware
Systems
Software
Vasudev Lal
Principal AI Research Scientist
Intel
Principal AI Research Scientist at Intel Labs where I lead the Multimodal Cognitive AI team. The Cognitive AI team develops AI systems that can synthesize concept-level understanding from multiple modalities: vision, language, video, etc. leveraging large-scale AI clusters powered by Intel AI HW (eg: Intel Gaudi-based AI clusters). Vasudev’s current research interests include self-supervised training at scale for continuous and high dimensional modalities like images, video and audio; mechanisms to go beyond statistical learning in today’s AI systems by incorporating counterfactual reasoning and principles from causality and exploring full 3D parallelism (tensor + parallel + data) for training and inferencing large AI models on Intel AI HW (eg: Intel Gaudi-based AI clusters in Intel Dev Cloud). Vasudev obtained his PhD in Electrical and Computer Engineering from the University of Michigan, Ann Arbor in 2012.
Intel
Website: https://www.intel.com/content/www/us/en/homepage.html
Intel create world-changing technology that improves the life of every person on the planet. Today they are applying their reach, scale, and resources to enable their customers to capitalize more fully on the power of digital technology. Inspired by Moore’s Law, Intel continuously work to advance the design and manufacturing of semiconductors to help address their customers’ greatest challenges. Power AI everywhere with Intel® with proven AI expertise, un unmatched partner ecosystem, and a comprehensive hardware and software portfolio. Intel can help you deliver the AI results you need.
Join Vamsi Boppana, AMD Senior Vice President of AI, as he unveils the latest breakthroughs in AI technology, driving advancements across cloud, HPC, embedded, and client segments. Discover the impact of strategic partnerships and open-source innovation in accelerating AI adoption. Through real-world examples, see how AI is getting developed and deployed, reshaping the global compute landscape from the cloud to the client.
Infrastructure
Hardware
Systems
Vamsi Boppana
SVP, AI
AMD
Vamsi Boppana is responsible for AMD’s AI strategy, driving the AI roadmap across the client, edge and cloud for AMD’s AI software stack and ecosystem efforts. Until 2022, he was Senior Vice President of the Central Products Group (CPG), responsible for developing and marketing Xilinx’s Adaptive and AI product portfolio. He also served as executive sponsor for the Xilinx integration into AMD.
At Xilinx, Boppana led the silicon development of leading products such as Versal™ and Zynq™ UltraScale™+ MPSoC. Before joining the company in 2008, he held engineering management roles at Open-Silicon and Zenasis Technologies, a company he co-founded. Boppana began his career at Fujitsu Laboratories. Caring deeply about the benefits of the technology he creates, Boppana aspires both to achieve commercial success and improve lives through the products he builds.
Building ever larger scale AI clusters hinges on addressing the challenge of creating reliable and fault-tolerant systems. This keynote will explore how Meta work on Llama 3: Herd of Models informs strategies for building robust AI infrastructure. We will highlight the Open AI Systems Initiative and Rack-scale Alignment for accelerator diversity focusing on areas of power, compute, and liquid cooling. Our discussion will emphasize the critical role of community engagement in setting open standards and interoperability. This session will provide a roadmap for developing AI systems that can withstand the unpredictability of real-world applications.
Infrastructure
Hardware
Systems
Dan Rabinovitsj
VP, Infrastructure
Meta
Dan has 30+ years’ experience in developing technology that connects people, with a particular focus on market disruption and innovation. Dan has served in executive leadership roles in Silicon Labs, NXP, Atheros, Qualcomm, Ruckus Networks and Facebook/Meta. Dan joined Meta in 2018 to lead Facebook Connectivity, a team focused on bringing more people online at faster speeds and changing the telecom industry through the Telecom Infra Project. Dan is now supporting a team developing and sustaining data center hardware and AI systems.
Systems
Infrastructure
Generative AI
Hardware
Software
Lip-Bu Tan
Founder & Chairman
Walden International
Lip-Bu Tan is Founder and Chairman of Walden International (“WI”), and Founding Managing Partner of Celesta Capital and Walden Catalyst Ventures, with over $5 billion under management. He formerly served as Chief Executive Officer and Executive Chairman of Cadence Design Systems, Inc. He currently serves on the Board of Schneider Electric SE (SU: FP), Intel Corporation (NASDAQ: INTC), and Credo Semiconductor (NASDAQ: CRDO).
Lip-Bu focuses on semiconductor/components, cloud/edge infrastructure, data management and security, and AI/machine learning.
Lip-Bu received his B.S. from Nanyang University in Singapore, his M.S. in Nuclear Engineering from the Massachusetts Institute of Technology, and his MBA from the University of San Francisco. He also received his honorary degree for Doctor of Humane Letters from the University of San Francisco. Lip-Bu currently serves on Carnegie Mellon University (CMU)’s Board of Trustees and the School of Engineering Dean’s Council, Massachusetts Institute of Technology (MIT)’s School of Engineering Dean’s Advisory Council, University of California Berkeley (UCB)’s College of Engineering Advisory Board and their Computing, Data Science, and Society Advisory Board, and University of California San Francisco (UCSF)’s Executive Council. He’s also a member of the Global Advisory Board of METI Japan, The Business Council, and Committee 100. He also served on the board of the Board of Global Semiconductor Alliance (GSA) from 2009 to 2021, and as a Trustee of Nanyang Technological University (NTU) in Singapore from 2006 to 2011. Lip-Bu has been named one of the Top 10 Venture Capitalists in China by Zero2ipo and was listed as one of the Top 50 Venture Capitalists on the Forbes Midas List. He’s the recipient of imec’s 2023 Lifetime of Innovation Award, the Semiconductor Industry Association (SIA) 2022 Robert N. Noyce Award, and GSA’s 2016 Dr. Morris Chang's Exemplary Leadership Award. In 2017, he was ranked #1 of the most well-connected executives in the technology industry by the analytics firm Relationship Science.
The exponential growth in compute demands of AI, and the move to Software Defined Products, means that more than ever workloads are defining the semiconductor requirements. The need to hit the restrictive power, performance, area and cost constraints of edge designs, mean that every element of the design needs to be optimized and co-designed with the workloads in mind. Additionally, the design needs to evolve even after semiconductor design is complete as it adapts to new demands.
In this presentation we will look at how semiconductor design is changing to enable rapid development and deployment of custom, application optimized, system-on-chip designs – from concept through to in-life operation, as we chart the path to a sustainable compute future.
Data Center
Hardware
Infrastructure
Ankur Gupta
Senior Vice President and General Manager
Siemens EDA
Ankur Gupta is Senior Vice President and General Manager of Digital Design Creation at Siemens EDA. This includes Test, Embedded Analytics, Digital IC design, Power Optimization, and Power Integrity Analysis. Formerly he was head of Product Management and Applications at Ansys, Semiconductor and Head of Applications Engineering for Digital Implementation & Signoff at Cadence Design Systems.
Ankur has 20+ years of experience in EDA, working on some of the industry’s most innovative Test, Digital Design, Implementation and Signoff products. He holds a Master’s Degree in Electrical and Computer Engineering, from Iowa State University.
Generative AI
Systems
Infrastructure
Thomas Sohmers
Founder and CEO
Positron AI
Thomas Sohmers is an innovative technologist and entrepreneur, renowned for his pioneering work in the field of advanced computing and artificial intelligence. Thomas began programming at a very early age, which led him to MIT as a high school student where he worked on cutting-edge research. By the age of 18, he had become a Thiel Fellow, marking the beginning of his remarkable journey in technology and innovation. In 2013, Thomas founded Rex Computing, where he designed energy-efficient processors for high-performance computing applications. His groundbreaking work earned him numerous accolades, including a feature in Forbes' 30 Under 30. After a stint exploring the AI industry, working on scaling out GPU clouds and large language models, Thomas founded and became CEO of Positron in 2023. Positron develops highly efficient transformer inferencing systems, and under Thomas's leadership, it has quickly become one of the most creative and promising startups in the AI industry.
Positron AI
Website: https://www.positron.ai/
Positron delivers vendor freedom and faster inference for both enterprises and research teams, by allowing them to use hardware and software explicitly designed from the ground up for generative and large language models (LLMs).
Through lower power usage and drastically lower total cost of ownership (TCO), Positron enables you to run popular open source LLMs to serve multiple users at high token rates and long context lengths. Positron is also designing its own ASIC to expand from inference and fine tuning to also support training and other parallel compute workloads.