Federated learning (FL) is a distributed machine learning strategy that generates a global
model by learning from multiple decentralized edge clients. FL enables on-device training …

Wind energy has emerged as a highly promising source of renewable energy in recent
times. However, wind turbines regularly suffer from operational inconsistencies, leading to …

High throughput serving of large language models (LLMs) requires batching sufficiently
many requests at a time. However, existing systems struggle because the key-value cache …

Large language models have led to state-of-the-art accuracies across several tasks.
However, training these models efficiently is challenging because: a) GPU memory capacity …

We present DietNeRF, a 3D neural scene representation estimated from a few images.
Neural Radiance Fields (NeRF) learn a continuous volumetric representation of a scene …

In the last three years, the largest dense deep learning models have grown over 1000x to
reach hundreds of billions of parameters, while the GPU memory has only grown by 5x (16 …

Large deep learning models offer significant accuracy gains, but training billions to trillions
of parameters is challenging. Existing solutions such as data and model parallelisms exhibit …

Recent developments in multimodal training methodologies, including CLIP and ALIGN,
obviate the necessity for individual data labeling. These approaches utilize pairs of data and …

Since hardware resources are limited, the objective of training deep learning models is
typically to maximize accuracy subject to the time and memory constraints of training and …

It is a challenging task to train large DNN models on sophisticated GPU platforms with
diversified interconnect capabilities. Recently, pipelined training has been proposed as an …