Four Architectural Opportunities for LLM Inference Hardware (Google)


A new technical paper titled "Challenges and Research Directions for Large Language Model Inference Hardware" was published by Google. Abstract "Large Language Model (LLM) inference is hard. The autoregressive Decode phase of the underlying Transformer model makes LLM inference fundamentally different from training. Exacerbated by recent AI trends, the primary challenges are memory and in... » read more

DL Compiler Framework For More Efficient Inter-Core Connected AI Chips (UIUC, Microsoft)


A new technical paper titled "Elk: Exploring the Efficiency of Inter-Core Connected AI Chips with Deep Learning Compiler Techniques" was published by researchers at the University of Illinois Urbana-Champaign (UIUC) and Microsoft Research. Abstract "To meet the increasing demand of deep learning (DL) models, AI chips are employing both off-chip memory (e.g., HBM) and highbandwidth low-laten... » read more