InfoWorld

Multi-token prediction technique triples LLM inference speed without auxiliary draft models

With reported 3x speed gains and limited degradation in output quality, the method targets one of the biggest pain points in production AI systems: latency at scale.

Researchers baked 3x inference speedups directly into LLM weights — without speculative decoding

Researchers from the University of Maryland, Lawrence Livermore, Columbia and TogetherAI have developed a training technique that triples LLM inference speed without auxiliary models or infrastructure ...
The Verge

The Switch is now Nintendo’s best-selling console of all time

The DS has been overthrown, 12 years after it was discontinued. The DS has been overthrown, 12 years after it was discontinued. is a news writer focused on creative industries, computing, and ...
IEEE

Attitude Estimation for Rigid Aircraft: A Distributed Finite-Time Complementary Filter With Multiple Inertial Measurement Units

Abstract: Despite recent advances in attitude estimation for a single inertial measurement unit (IMU), obtaining precise attitude estimation with multiple IMUs in the presence of gyro bias remains a ...
IEEE

Distributed Fixed-Time Algorithms for Time-Varying Constrained Optimization Problems

Abstract: In this article, the distributed form of the zeroing neural network for solving time-varying optimal problems is put forward. Compared with traditional centralized algorithms, distributed ...