Building Fixed HW Implementations of Neural Networks (Yale, Cornell et al.)


Researchers from Yale University, Cornell University, Boston University, and NTT Research have published “Physical Foundation Models: Fixed hardware implementations of large-scale neural networks”. Abstract "Foundation models are deep neural networks (such as GPT-5, Gemini~3, and Opus~4) trained on large datasets that can perform diverse downstream tasks -- text and code generation, q... » read more

Foundation Model For Physics: The Next Layer Of Intelligence For Engineering


Over the past decade or so, foundation models have emerged as the dominant paradigm for interacting with language, images, and code. Large Language Models (LLMs) can generate text. Vision models can interpret images. Multimodal systems can connect the two seamlessly. But one domain has not yet seen the same foundation-model-level shift: validated, deterministic reasoning over the physical wo... » read more

Building AI Without Guardrails


Key Takeaways: AI governance is broadly recognized as essential, but today it remains fragmented, largely aspirational, and lacking enforceable mechanisms for accountability, runtime assurance, and global interoperability. Because AI innovation is advancing too quickly for governments or standards bodies to keep pace, practical AI governance is most likely to emerge first from high‑ri... » read more

RAG-Enabled AI Stops Hallucinations, Adds Sources


Many EDA companies have taken the first steps to incorporate generative AI into their tools, and in such tightly controlled environments GenAI appears to have great benefits. But its broader adoption has been delayed by its notorious inaccuracy, giving results that are often out of date, untrue, and unsourced. That's starting to change. GenAI is evolving so rapidly that these kinds of proble... » read more