Google's new TurboQuant algorithm speeds up AI memory 8x, cutting costs by 50% or more

Within 24 hours of the release, community members began porting the algorithm to popular local AI libraries like MLX for Apple Silicon and llama.cpp.

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

Ultrasonic Tomography System for Nut Defect Detection Using Linear Arrays

Abstract: Conventional phased-array ultrasound can detect defects in nuts; however, accurately reconstructing their irregular shapes and precise spatial structures remains challenging. To faithfully ...
IEEE

Real-time Implementation of Phased Array Radar Tracking Algorithm Based on Embedded GPU

Abstract: The embedded Graphics Processing Unit (GPU) module, which includes both Central Processing Unit (CPU) and GPU processors, can be easily integrated into radar systems, offering high ...