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

Minimum Cost First Algorithms for Multi-Function Phased Array Radar Scheduling

Abstract: Earliest Start Time (EST) and Earliest Deadline (ED) are two classical radar task scheduling algorithms, where task priorities are not considered at all. In this paper, we propose the ...
IEEE

Photonically Assisted 2D Transmit Array Using Fourier-Based Beamformer

Abstract: This paper developed a photonically actuated ultrawideband, two-dimensional transmit array using a Fourier optical lens as a beamformer. Data encoding was developed, in which custom IF ...