category: Top Stories

category: Auto, Security & Edge AI

Designing Chips In The Context Of Rapidly Evolving AI
By Ann Mutschler - 04 May, 2026 - Comments: 0

Key Takeaways: Agentic edge AI drives long-lived, tool-mediated loops with variable demands for compute, tokens, and memory. Edge PPA is dominated by memory hierarchy and data movement, forcing tight feature triage and robust RAS. Rapid model churn (multimodal, MoE, new formats) requires programmable, headroom-rich compute, interconnect, and runtime. Experts At The Table: Ch... » read more

Creating Agentic EDA Methodologies
By Brian Bailey - 30 Apr, 2026 - Comments: 1

Key takeaways Agentic methodologies need to be able to reason across multiple data formats and abstractions. It is not clear how much data from previous designs is useful in new designs. Standards may help, but the lack of them may only impact cost. The relationship between tools and methodologies is bidirectional. Tools enable methodologies, and methodologies are dependent ... » read more

NoC Coherency Challenges Balloon With AI SoCs And Chiplets
By Liz Allan - 30 Apr, 2026 - Comments: 0

Key Takeaways Data movement, congestion, and energy efficiency are key determiners of whether compute is usable. Different processors bring various coherency challenges. For example, a cache-coherent NoC for CPUs is expensive and harder to verify than an I/O-coherent NoC for an accelerator. Designers need to balance top-down performance with bottom-up physical engineering to effect... » read more

How Long Will CAN Stick Around As Rival Networks Speed Up?
By Liz Allan - 29 Apr, 2026 - Comments: 0

Key Takeaways Automotive Ethernet is rapidly becoming the backbone of software-defined vehicles for higher bandwidth, scalability, and advanced features like TSN and security that legacy protocols cannot match. CAN, LIN, and other legacy networks will not disappear quickly because they are deeply embedded, low‑cost, and proven, but they are increasingly seen as inadequate for future A... » read more

When Semiconductor Materials Misbehave
By Gregory Haley - 27 Apr, 2026 - Comments: 0

Key Takeaways Material behavior in production depends on the process context that no development environment can fully replicate. In advanced packaging, the interactions that cross domain boundaries are increasingly where failures originate. The most accurate materials data is also the most commercially sensitive, leaving simulation models calibrated against generic inputs rather tha... » read more

Can Edge AI Keep Up?
By Ann Mutschler - 23 Apr, 2026 - Comments: 0

Key Takeaways: Model development is outpacing silicon design cycles, so edge AI architectures must prioritize adaptability. The required cadence for model updates is highly application-dependent and is closely tied to product lifetime and operational risk. Adaptability can conflict with power, performance, and area targets, so effective heterogeneous architectures and robust softwa... » read more

Chiplet Standards Aim For Plug-n-Play
By Bryon Moyer - 16 Apr, 2026 - Comments: 0

Key Takeaways Die-to-die chiplet standards are only the beginning. Many more standards are necessary for a chiplet marketplace. A number of such standards have either had initial versions released or are in progress. Existing work covers packaging, a system architecture, various design kits, a universal link layer, and updates to BoW. Today’s chiplets exist in silos. In a ... » read more

Silicon Photonics Lights The Way To More Efficient Data Centers
By Katherine Derbyshire - 16 Apr, 2026 - Comments: 0

Key Takeaways Photonic interconnects potentially increase bandwidth density while significantly reducing power consumption. AI workloads are driving their adoption in data centers. On the other hand, photonic interconnects require a variety of different materials, introducing process compatibility and thermal and mechanical stress issues. Integrated electro-optical I/O modules are th... » read more

AI Growing Impact On Chip Design And EDA Tools
By Ed Sperling - 15 Apr, 2026 - Comments: 0

Key Takeaways Many workflows in the data center are customer-specific, which is part of the reason there is so much interest in agentic AI-enabled tools. Large systems companies are pressing EDA vendors for performance improvements to keep pace with their AI workflows. The makeup of design teams is changing as AI infiltrates more of the chip design process. Experts at the Ta... » read more

Startup Funding: Q1 2026
By Jesse Allen - 13 Apr, 2026 - Comments: 0

The new year started off with a bang for private semiconductor companies, with 18 garnering mega funding rounds exceeding $100 million, and two, Rapidus and Cerebras, reaching the $1 billion mark. Predictably, the vast majority of those are either designing chips primarily for AI inference workloads or attempting to overcome bandwidth limitations by improving interconnects from the chip level t... » read more

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