Researchers from Yale University, Cornell University, Boston University, and NTT Research have released “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, question answering, summarization, image... » read more The post Building Fixed HW Implementations of Neural Networks (Yale, Cornell et al.) appeared first on Semiconductor Engineering .
Source: Semiconductor Engineering — read the full report at the original publisher.