On-device large language models (LLMs), referring to running LLMs on edge devices, have
raised considerable interest since they are more cost-effective, latency-efficient, and privacy …

The past decade has witnessed the rapid development and adoption of machine and deep
learning (ML & DL) methodologies in agricultural systems, showcased by great successes in …

Abstract Large Language Models (LLMs) have transformed natural language processing
tasks successfully. Yet, their large size and high computational needs pose challenges for …

LLMs are seeing growing use for applications which require large context windows, and with
these large context windows KV cache activations surface as the dominant contributor to …

Large language models (LLMs) have revolutionized natural language processing tasks.
However, their practical deployment is hindered by their immense memory and computation …

Large Language Models (LLMs) employ auto-regressive decoding that requires sequential
computation, with each step reliant on the previous one's output. This creates a bottleneck …

This paper addresses the growing need for efficient large language models (LLMs) on
mobile devices, driven by increasing cloud costs and latency concerns. We focus on …

Large foundation models, including large language models (LLMs), vision transformers
(ViTs), diffusion, and LLM-based multimodal models, are revolutionizing the entire machine …

Efficiently serving large language models (LLMs) requires batching of many requests to
reduce the cost per request. Yet, with larger batch sizes and longer context lengths, the key …

In the rapidly evolving landscape of artificial intelligence (AI), generative large language
models (LLMs) stand at the forefront, revolutionizing how we interact with our data. However …