We introduce a novel superpoint-based transformer architecture for efficient semantic
segmentation of large-scale 3D scenes. Our method incorporates a fast algorithm to partition …

On-device machine learning (ML) promises to improve the privacy, responsiveness, and
proliferation of new, intelligent user experiences by moving ML computation onto everyday …

On-device machine learning (ML) moves computation from the cloud to personal devices,
protecting user privacy and enabling intelligent user experiences. However, fitting models …

To deploy machine learning models on-device, practitioners use compression algorithms to
shrink and speed up models while maintaining their high-quality output. A critical aspect of …

Learning-in-memory (LIM) is a recently proposed paradigm to overcome fundamental
memory bottlenecks in training machine learning systems. While compute-in-memory (CIM) …

Anotácia: Hlboké architektúry neurónových sietí preukázali schopnosť napodobňovať ľudí
pri rôznych úlohách (rozpoznávanie obrazu, opis scény, alebo hranie hier). Veľký nárast …