As AI inference moves beyond hyperscale data centers into edge environments such as robotics, drones, industrial automation, and intelligent infrastructure, system architects face a new challenge: delivering high-performance AI within tight power, cost, and latency constraints. This white paper explores 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.

Download this white paper to learn:

  • Why LPDDR memory has emerged as the optimal balance of bandwidth, power efficiency, and scalability for edge AI inferencing
  • How DEEPX, Rambus, and Samsung Foundry co-engineered a validated memory platform spanning multiple AI processor generations and process nodes
  • Why tightly integrating memory architecture, AI compute, and manufacturing technology is becoming essential for deploying advanced generative and multimodal AI at the edge

Enabling Efficient Edge AI Inferencing Through Ecosystem Collaboration