Google's TurboQuant compression tech cuts LLM memory use by 6x with no accuracy loss

The biggest memory burden for LLMs is the key-value cache, which stores conversational context as users interact with AI ...

Google reveals algorithms to address AI memory challenges; memory and storage stocks drop

Google unveils TurboQuant, PolarQuant and more to cut LLM/vector search memory use, pressuring MU, WDC, STX & SNDK.
IEEE

Parallel Time-Domain Simulation Algorithm Based on Physics-Informed Neural Networks

Abstract: This letter proposes a parallel physics-informed neural network (PINN) algorithm for solving time-domain electromagnetic simulations. This method first decomposes the global computational ...
IEEE

A Parallel Decoupling Self-planning Search-attack Algorithm for Multi-UAV with Reward Estimation

Abstract: This paper proposes a novel algorithm for multiple unmanned aerial vehicles (multi-UAV) cooperative search-attack missions operated in uncertain environments. A parallel decoupling ...