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

Performant Side-Channel Resistant RISC-V Core to Secure Neural Network Inference (Northeastern Univ.)


A new technical paper titled "PermuteV: A Performant Side-channel-Resistant RISC-V Core Securing Edge AI Inference" was published by researchers at Northeastern University. Abstract "Edge AI inference is becoming prevalent thanks to the emergence of small yet high-performance microprocessors. This shift from cloud to edge processing brings several benefits in terms of energy savings, impr... » read more