An Exploration of Agent Scaling for HLS Design Space Exploration (IBM)


A new technical paper, "Agent Factories for High Level Synthesis: How Far Can General-Purpose Coding Agents Go in Hardware Optimization?" was published by IBM. Abstract "We present an empirical study of how far general-purpose coding agents – without hardware-specific training – can optimize hardware designs from high-level algorithmic specifications. We introduce an agent factory, a ... » read more

AI Won’t Kill Verification IP, But It Will Redefine It


Key Takeaways AI will enhance, not replace, verification IP by automating test generation and debug. Verification IP’s core value will increasingly lie in trust, accountability, and system-level realism, especially as designs become more complex, multi-die, and security-sensitive. AI shifts verification bottlenecks from execution to specification quality, raising expectations for c... » read more

How AI Will Automate Chip Design


AI has been used in EDA for many years for the core algorithms in tools, but it's getting smarter and more optimized with the rollout of generative and agentic AI. As it evolves and improves, hardware engineers are finding ways to leverage it for more complex tasks. Ziyad Hanna, corporate vice president at Cadence, talks about five levels of autonomy in chip design that mirror those in the auto... » read more

Human-Centered Agentic AI Workflows For RTL Verification


Productivity challenges in modern semiconductor development stem less from individual tool limitations and more from process-level complexity across design creation, verification, and iteration. Agentic EDA addresses this shift by embedding intelligence directly into workflows that span creation and validation. The Questa One Agentic Toolkit extends the Questa One solution with human-centere... » read more

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

A Framework That Generates Chip Layouts Directly From Natural Language Specifications (U. of Bristol, RAL)


A new technical paper, "NL2GDS: LLM-aided interface for Open Source Chip Design," was published by researchers at University of Bristol and Rutherford Appleton Laboratory. Abstract "The growing complexity of hardware design and the widening gap between high-level specifications and register-transfer level (RTL) implementation hinder rapid prototyping and system design. We introduce NL2GDS (... » read more

Follow The AI Leader


In the 1980s, a common expression was "nobody ever got fired for buying IBM." It was considered the safe option, long after new technologies had emerged. While it may not have been the most advanced option available, it remained the safe bet. It had an established ecosystem, and it was a known quantity. But who or what is the safe bet when it comes to AI? Who has the necessary data? Who has ... » read more

Using Data And AI More Effectively In EDA


Key Takeaways The data being produced by EDA tools tends to be for human consumption and has weak semantics. Agents are attempting to create actionable information from unstructured data. The Model Context Protocol may provide AI with access to better data. Semiconductor design generates a lot of data, but how much of that is useful or currently being used by AI tools? And h... » read more

The Race Begins For Much Bigger Abstractions In Data Centers


Key Takeaways Data center build-out is enabling much larger and more complex abstractions. Competition is building for digital/virtual twins across multiple industry segments, including automotive, aerospace, and chip manufacturing. AI, and particularly AI agents, will play a significant role in sorting through data to find potential trouble spots. The frenzy of new data cen... » read more

AI’s Impact On Engineering Jobs May Be Different Than Expected


Key Takeaways: AI is expected to eliminate many repetitive, entry-level tasks, but that may allow engineering students trained on the latest tools to start in more senior positions. AI is a force multiplier. It can accelerate the learning curve for junior engineers. While AI is very good at solving multi-dimensional problems, domain expertise, critical thinking, and sanity checks wil... » read more

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