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Semiconductor Engineering: AI Energy Gap and Chiplets: Why Data Movement Matters

AI Energy Gap and Chiplets: Why Data Movement Matters

At the panel discussion at Chiplet Summit 2026, where Arteris was among the experts, participants emphasized that efficient AI chiplets require more than fast physical links like UCIe—they demand smart, system-level architecture around data movement, coherency, and protocol choice. As AI compute outpaces energy efficiency gains, moving data between dies is becoming a major power challenge, making interconnect design a critical factor in overall system performance and efficiency. Learn more in the article.

Electronic Design: Bridging the Gap to Chiplet Interoperability

Bridging the Gap to Chiplet Interoperability | Electronic Design

This article examines the gap between today’s tightly managed multi-die implementations and the long-term vision of true multi-vendor chiplet interoperability. It explains how companies are deploying homogeneous and heterogeneous architectures, why proprietary flows still limit plug-and-play integration, and how standards like UCIe address only part of the challenge. The piece also highlights the role of scalable interconnects, NoC architectures, and Arteris system IP and automation solutions in building the foundation for a more open chiplet ecosystem. Learn more in the article.

EE Times: The Data Dilemma: Cracking the Code of Data Movement for the Next Wave of Semiconductor Innovation

The Data Dilemma: Cracking the Code of Data Movement for the Next Wave of Semiconductor Innovation

As semiconductor architectures scale toward chiplets, multi-die SoCs, and AI-driven workloads, data movement—not raw compute—has emerged as the primary limiter of performance, power, and scalability. The article argues that network-on-chip fabrics are becoming the true center of gravity in modern designs, responsible for moving massive volumes of data efficiently across XPUs, memory hierarchies, and specialized logic while staying within strict energy and thermal budgets. Learn more in the article.

Semiconductor Engineering: Solving Real-World AI Bottlenecks

SoC performance is dependent upon data availability

This article explains how modern AI SoCs are increasingly limited by data movement and memory latency rather than raw compute. It highlights the role of efficient interconnects and shared last-level caches in reducing latency, power consumption, and DRAM traffic, and positions FlexGen, FlexNoC, and CodaCache as an integrated system IP approach for keeping data close to compute in complex, real-time designs. Learn more in the article.

EDN: AI workloads demand smarter SoC interconnect design

AI workloads demand smarter SoC interconnect design - EDN

This EDN article explains how AI workloads are pushing traditional SoC interconnect design beyond practical limits, making intelligent automation essential. Physically aware NoC algorithms optimize topology, power, latency, and timing closure, enabling scalable AI SoCs from data centers to the edge, with Arteris highlighted as a leader in automated interconnect innovation. Learn more in the article.