As large language models (LLMs) like ChatGPT continue to advance, user expectations of them keep growing, including with ...

Innovative software engineer focused on optimizing search performance in dynamic environments. This article highlights key ...

The resulting benefit can vary accordingly. “A small simple branch predictor might speed up a processor by 15%, whereas a ...

Data partitioning is the most fundamental procedure before parallelizing complex analysis on very big graphs. As a classical NP-complete problem, graph partitioning usually employs offline or ...

By leveraging the inherent parallelism of the LTB map and parallelizing encryption operations, coupled with efficient hardware implementation on FPGAs, our encryption method achieves high-speed ...

Diffusion models are widely used in many AI applications, but research on efficient inference-time scalability, particularly for reasoning and planning (known as System 2 abilities) has been lacking.

As demand grows for faster, more capable large language models (LLMs), researchers have introduced a new approach that significantly reduces response times without compromising output quality. The ...

Options include parallelizing the computation, using the (incubating) Vector API, memory-mapping different sections of the file concurrently, using AppCDS, GraalVM, CRaC, etc. for speeding up the ...

KAIST (President Kwang Hyung Lee) announced on the 20th that a research team led by Professor Sungjin Ahn in the School of Computing has developed a new technology that significantly improves the ...

Big levers have been pulled, and power is now flowing through the heart of Isambard-AI, UK’s brand new AI supercomputer that carries NVIDIA’s powerful hardware and sophisticated software stack.