Download this white paper to learn how organizations can establish trust across every device in AI infrastructure and modern data centers as heterogeneous processors, accelerators, and controllers become increasingly prevalent. Discover how the open-source Caliptra Root of Trust framework provides a common foundation for device identity, measured boot, and attestation across data center-class systems, and explore the challenges and best practices for transforming an open standard into a production-ready deployment. Learn how enterprises can stay aligned with the Caliptra ecosystem while accelerating implementation, reducing integration risk, and achieving enterprise-grade security for data center and AI infrastructure.
Papers
Enabling Efficient Edge AI Inferencing Through Ecosystem Collaboration
Download this white paper to learn how DEEPX, Rambus, and Samsung Foundry collaborated to create a scalable LPDDR-based memory platform optimized for edge AI inferencing. By aligning AI processor architecture, memory interface IP, and advanced foundry technology early in the design cycle, the companies reduced integration risk, accelerated development, and enabled a platform that scales from today’s LPDDR5-based designs to next-generation LPDDR5X solutions on advanced process nodes. The paper demonstrates how memory subsystem design has become a critical differentiator for edge AI performance and illustrates why ecosystem-level collaboration is emerging as a key driver of innovation for future intelligent systems.
Data Center Evolution: DDR5 DIMMs Advance Server Performance
Download this white paper to explore how DDR5 DIMMs address these emerging requirements, delivering significant advancements in data rate, density, power efficiency, and channel architecture over DDR4. It examines the role of DDR5 as the foundational memory layer in modern AI infrastructure, complementing heterogeneous compute architectures and supporting highly concurrent, data-driven workloads, while also outlining the key design considerations required to achieve reliable, high-performance operation in next-generation servers.
Securing the Software Defined Vehicle: How Rambus and Telechips Enable Safe, Scalable Automotive SoC
Automotive architectures are rapidly shifting from distributed ECUs to centralized, zonal computing models designed for the software-defined vehicle (SDV). As workloads such as AI-driven ADAS, digital cockpits, and OTA updates increase system complexity, automakers are consolidating functions onto high-performance SoCs, improving efficiency but raising new challenges in isolation and security. To address this, Telechips is developing scalable, heterogeneous SoC platforms with built-in, system-level security, including the hardware-based Root of Trust, the Rambus RT-648 incorporating an Arm Cortex-M33 processor enabling secure boot, robust key management, and trusted system operation.
Hardware Deployment for Secure AI Using Confidential Computing
AI’s fast evolution is producing autonomous systems that can operate with minimal human oversight, improve themselves and become effective at decision-making in complex environments. These developments require careful consideration of security and privacy. To limit the overhead performance impact (area, throughput, latency and power), hardware-based security solutions can be deployed using state-of-the-art silicon IP.
HBM4E Memory: Break Through to Greater Bandwidth
AI/ML’s demands for greater bandwidth are insatiable driving rapid improvements in every aspect of computing hardware and software. HBM memory is the ideal solution for the high bandwidth requirements of AI/ML training, but it entails additional design considerations given its 2.5D architecture. Now we’re on the verge of a new generation of HBM that will raise memory and capacity to new heights. Designers can realize new levels of performance with the HBM4E-ready memory subsystem solution from Rambus.
