Large Language Models (LLMs) have demonstrated remarkable capabilities across various
fields, from natural language understanding to text generation. Compared to non-generative …

Memory-centric computing aims to enable computation capability in and near all places
where data is generated and stored. As such, it can greatly reduce the large negative …

Large language models (LLMs) have emerged due to their capability to generate high-
quality content across diverse contexts. To reduce their explosively increasing demands for …

Graph Neural Networks (GNNs) are emerging models to analyze graph-structure data. GNN
execution involves both compute-intensive and memory-intensive kernels. The latter kernels …

The rapid development of large language models (LLMs) has significantly transformed the
field of artificial intelligence, demonstrating remarkable capabilities in natural language …

Recently, there has been an extensive research effort in building efficient large language
model (LLM) inference serving systems. These efforts not only include innovations in the …

The growing memory and computational demands of large language models (LLMs) for
generative inference present significant challenges for practical deployment. One promising …

Large language models (LLMs) are widely used for natural language understanding and
text generation. An LLM model relies on a time-consuming step called LLM decoding to …

Large Language Model (LLM) inference uses an autoregressive manner to generate one
token at a time, which exhibits notably lower operational intensity compared to earlier …

Generative Artificial Intelligence (GenAI) applies models and algorithms such as Large
Language Model (LLM) and Foundation Model (FM) to generate new data. GenAI, as a …