Human-Centered Agentic AI Comes To RTL Verification


For decades, productivity gains in electronic design automation (EDA) came from better engines. Faster solvers, higher-capacity simulators, and more scalable formal tools allowed design and verification teams to keep pace as designs grew larger. That model is no longer sufficient. Today’s design and verification bottleneck is not raw tool performance, but the coordination overhead required... » read more

CPO Is Extending The Limits Of What’s Possible In AI Data Centers


Key Takeaways I/O architecture must be co-designed with compute from day one. Partitioning SoCs into heterogeneous chiplets (compute, EIC, PIC, lasers) directly affects power delivery, floor-planning, interconnect topology, and system scalability. Successful CPO designs require architects to think in multi-physics terms, balancing electrical signaling, thermal stability, optical beha... » read more

AI Power on the Edge


Key takeaways Power and thermal become primary design considerations, not just optimizations. Hardware architectures need to be developed from the ground up. Hardware/software/model co-development is essential. Implementing AI on the edge is driven by a different set of metrics than training or even inference in the cloud. It makes power a first-class citizen, if not the mos... » read more

Data-Driven Optimization In Semiconductor Manufacturing


Effectively scaling semiconductor manufacturing is critical to meeting the rapidly growing demand and requires solving numerous technical challenges. The substantial capital investment required for semiconductor manufacturing further complicates the business equation. Legacy fabs are designed redundantly to maintain the uptime required for reliable ROI, further increasing costs. Producers want ... » read more

Improving Yield Through Shared Data


Increasing complexity due to advanced packaging, multi-die assemblies, and more devices under test is having an impact on yield, which in turn slows time to market and impacts overall chip costs. What's needed is a way to share data that previously was siloed by chipmakers, fabs, and OSATs. Jayant D'Souza, technical product director at Siemens EDA, talks about the underlying drivers for sharing... » read more

Digital Twins: The Cloud’s The Limit


Key Takeaways Digital twins are gaining traction as a way of testing different options at every step of the design-through-manufacturing flow. AI can be used to glue together disparate data types in multi-physics simulations. The promise of digital twins is huge, but multiple challenges need to be solved before it can live up to its potential. Digital twin technology is draw... » read more

The Future of Semiconductors: Engineering in the Convergence era


The semiconductor industry is entering a convergence era where silicon, software, physics, packaging, security, AI, and power constraints all intertwine. Device scaling still matters but architecture, integration, verification, and automation will define the industry’s trajectory. Organizations that embrace this cross-domain, lifecycle-oriented mindset will define the next decade. Moore’... » read more

Chip Industry Week In Review


Think tank IAPS' report on AI integrity attacks contends that advanced AI systems must be protected from hidden tampering, backdoors, or unauthorized changes that could alter their behavior or outputs, especially when AI adoption is scaling rapidly, with over 60% of the federal workforce now using AI every day. Geopolitics The U.S. government has drafted new export rules that may give W... » read more

Auto Security Accelerates With Standardization And Certified Silicon


Key Takeaways The automotive sector is actively developing and delivering secure parts and features ranging from secure boot to encrypted data and in-network protections. The cost of a breach can involve everything from ransomware to liability and/or damage to a brand. New standards are being introduced to ensure security, and technology developers are integrating cybersecurity requi... » read more

New Automotive Architectures Are Shaking Up Processor And Memory Choices


Key Takeaways Assisted and autonomous driving require more data from more sensors, and much faster processing of some of that data. The shift to software-defined vehicles and centralized intelligence makes it easier to identify where the most advanced processors and memories are required, and where older and less expensive technologies can be deployed. Technologies that were largely ... » read more

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