Semiconductor Engineering

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

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 ...

TTT-Discover optimizes GPU kernels 2x faster than human experts — by training during inference

A new technique from Stanford, Nvidia, and Together AI lets models learn during inference rather than relying on static ...
NextBigFuture

Distributed AI Inference Will Capture Most of the LLM Value

Using the AIs will be way more valuable than AI training. AI training – feed large amounts of data into a learning algorithm to produce a model that can make predictions. AI Training is how we make ...