tag: AI

Verification Methodologies Struggle To Keep Up With AI
By Brian Bailey - 25 Jun, 2026 - Comments: 0

Key Takeaways:  The rapid development of AI has resulted in new capabilities being provided to verification teams, beyond their ability to rationally insert them into accepted methodologies.  There is a lot of uncertainty about who will benefit the most from this technology. Is AI a junior engineer replacement or an enhancer?  The biggest benefits will come when AI helps engineers... » read more

More Massive Still: Why AI Infrastructure Demands A Unified Design Approach
By Antti Lautanen - 25 Jun, 2026 - Comments: 0

At the recent Data Center World 2026 in Washington, D.C., one message came through louder than ever: AI infrastructure is scaling faster than any system we’ve built before—and the industry can no longer afford to design it in silos. The workshop: “More Massive Still! Delivering AI-Driven Scale in the Face of Historic Constraints” captured this perfectly: the industry is shifting fr... » read more

Introducing An Agentic LLM For Chip Design
By Tanay Biradar - 25 Jun, 2026 - Comments: 0

By Tanay Biradar, Surya Gunukula, Tengxiao Liu, and Kexun Zhang ChipAgents has introduced Renoir, an agentic large language model (LLM) whose name means "renew." In early chip design benchmarks, Renoir outperforms the base model it was trained on and cuts costs by more than half. Furthermore, it can run entirely on-premises, allowing semiconductor companies to develop faster without compromi... » read more

Cloud HPC For AI: Addressing Latency, Cost, And Scale At The Architectural Level
By Sumit Vishwakarma - 22 Jun, 2026 - Comments: 0

Many organizations assume that moving HPC workloads to the cloud is simply a matter of lifting and shifting on-premises clusters. In practice, that approach often erodes performance, inflates costs, and undermines AI training efficiency. Getting the most out of HPC in the cloud requires a fundamentally different architectural approach — one that minimizes latency, maximizes utilization, an... » read more

Agentic AI Is Changing Data Center Architectures
By Ann Mutschler - 11 Jun, 2026 - Comments: 0

Key Takeaways: The rise of agentic AI is shifting data centers from GPU-centric number crunching to CPU-driven orchestration, where managing long-running reasoning loops and context is just as important as raw compute. Integrating CPUs, GPUs, and stacked memory into tightly coupled multi-die architectures with varying workloads makes it much harder to ensure they will be reliable and ef... » read more

Clocked DDR5 Client Memory Modules Enable Scaling To 9600 MT/s For AI PCs
By Carlos Weissenberg - 11 Jun, 2026 - Comments: 0

AI PCs are driving a new class of client workloads that behave very differently from traditional productivity or multimedia applications. Agentic AI systems are expected to plan, execute, and adapt in real time, maintaining persistent context while orchestrating multiple concurrent tasks. These usage patterns place sustained pressure on the memory subsystem, requiring not only higher peak bandw... » read more

How To Start Building Edge-Native AI
By Sharad Chole - 11 Jun, 2026 - Comments: 0

Cloud AI enables features like voice assistants and recommendations via centralized data centers, but it relies on consistent network connectivity, which often fails in real-world conditions. Edge-native AI shifts inference to devices such as phones, cars, and sensors, enabling real-time processing, enhanced privacy, and operational resilience. Why edge AI outpaces cloud Edge AI addresses key... » read more

Building A Production-Ready Optically Connected Rack For AI Scale-Up
By Nandita Aggarwal - 11 Jun, 2026 - Comments: 0

By Nandita Aggarwal and Nicholas Chang As AI models drive compute demand, servers keep getting bigger. Rack‑scale AI systems (such as the 72-GPU systems from NVIDIA or AMD) enable many GPUs to work together through system-level optimization. They push beyond the limits of single-chip performance and meet the soaring compute needs of the AI era. But this is just the beginning. The next s... » read more

Beyond The Demo: Deploying And Evaluating Open-Source AI Workloads
By Odin Shen - 11 Jun, 2026 - Comments: 0

As more open-source AI models move closer to real-world adoption, developers are changing how they evaluate edge deployment. The question is no longer simply whether a model can run, but whether it can be deployed reproducibly on a concrete platform, observed in practice, and turned into meaningful deployment decisions based on actual technical evidence. For developers, the CIX Armv9 platfor... » read more

Building Multi-Agent Systems For ASIC Flows
By Ed Sperling - 10 Jun, 2026 - Comments: 0

If one AI agent can solve a problem in a certain amount of time, can multiple agents solve it faster? The answer is yes, but only if the agents have well-defined roles and targets. This is where orchestrators fit in, and why they are so critical to agentic AI. Kexun Zhang, head of research at ChipAgents, talks about what exactly AI agents are, how they can be used to solve big problems that wou... » read more

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