Outlier-aware Quantization Framework Co-designed With Heterogeneous NVM For SLM Deployment on Edge Platforms (UCSD et al.)


  A new technical paper titled "QMC: Efficient SLM Edge Inference via Outlier-Aware Quantization and Emergent Memories Co-Design" was published by researchers at University of California San Diego and San Diego State University. Abstract "Deploying Small Language Models (SLMs) on edge platforms is critical for real-time, privacy-sensitive generative AI, yet constrained by memory, ... » read more