Raj Uppala, Senior Director of Marketing and Partnerships at Rambus, explores the complexities of IP licensing for emerging data center and AI applications. He discusses the growing demand for specialized IP components such as memory controllers, security solutions, and high-performance interfaces driven by the rise of AI accelerators and SoCs. Raj highlights the key factors customers consider when evaluating IP solutions, such as time-to-market, scalability, interoperability, and cost-effectiveness. The presentation underscores the importance of choosing the right IP solutions to accelerate innovation and streamline development in high-performance computing and AI infrastructure.
Navigating the Dynamics of IP Licensing for Data Center & AI
Why PCIe and CXL are Critical Interconnects for the AI Era
Lou Ternullo, Senior Director of IP Product Management at Rambus, discusses the critical role of PCIe and CXL interconnects in enabling the AI-driven data centers of tomorrow. He explores the challenges of traditional server setups, such as inefficiencies in resource allocation, over-provisioning, and data replication, and presents disaggregation as a solution to improve cost-efficiency and performance. By leveraging PCIe and CXL interconnects, data centers can enable heterogeneous compute, memory sharing, and lower-latency communication across servers and racks. Lou underscores the importance of these interconnect technologies in reducing latency, optimizing resource use, and lowering costs in AI-focused data centers.
The Road Ahead for Main Memory in the Data Center
Carlos Weissenberg, Product Marketing Manager for Memory Interface Chips at Rambus, discusses the increasing demands for memory driven by AI and high-performance computing. Carlos will explain the innovations incorporated in DDR5 memory modules, such as the dual-channel architecture and improved power management, designed to meet the growing memory requirements of data center servers. He will also discuss how the DDR5 memory interface chipset enables higher performance and greater capacity required by advanced server workloads.
Why Memory Matters for AI
In this roundtable discussion, memory experts Steve Woo, John Eble, and Nidish Kamath explore the critical role of memory in AI applications. They discuss how AI’s rapid evolution, especially with the growth of large language models, is driving the need for higher memory capacity, bandwidth, and power efficiency.
The Impact of AI 2.0 on Memory & Interconnect Technology
Steven Woo, Fellow and Distinguished Inventor at Rambus Labs, explores the transformative impact of AI 2.0 on memory and interconnect technology. He highlights how the rapid growth of AI models, exceeding trillions of parameters, and the shift to multimodal systems has dramatically increased the demand for higher memory capacity and bandwidth. He discusses the critical role of efficient data movement, the challenges in powering high-performance AI systems, and the importance of security as the value of AI models continues to rise.
Addressing AI’s Insatiable Demand For Power
The growth of AI has been staggering, and applications are emerging across the industry that offer new generative AI capabilities powered by large language models. The impact of these AI 2.0 applications is broad and fundamentally alters the way we interact with computers. However, this improvement in capabilities and performance has also been accompanied by massive increases in power consumption.
