The AI market is on track to continue explosive growth through at least 2032. Computing breakthroughs in various applications, such as data center accelerators for LLM training and inference, edge computing with automotive driver assistance, and the rise of robotics, drive this momentum.
Lately, generative AI has been fueling advancements in semiconductor design by pushing the limits of silicon integration, while physical AI systems are emerging, integrating perception, decision-making, and actuation under hard real-time and safety constraints. Physical AI expands beyond inference-centric edge AI, demanding deterministic latency, sustained execution, and fault containment at the silicon interconnect level.
Efficiently and securely connecting various XPU compute cores and memories is essential in the development of AI/machine learning chiplets and multi-die SoCs. Effective and secure data movement depends on many factors, and only high-performance silicon lets AI developers scale systems dynamically and meet the surging demands of complex AI workloads, including physical AI.
Download the Arteris AI Solution Brief and explore how Arteris solutions help align AI design demands for performance, security, and low TCO across the entire infrastructure: