Despite significant advances, training deep learning models remains a time-consuming and
resource-intensive task. One of the key challenges in this context is the ingestion of the …

Training deep learning models is a repetitive and resource-intensive process. Data
scientists often train several models before landing on set of parameters (eg, hyper …

In this work, we look at how to effectively manage and utilize deep learning models at each
edge location, to provide performance guarantees to inference requests. We identify …

Deep Neural Networks (DNNs) are witnessing increased adoption in multiple domains
owing to their high accuracy in solving real-world problems. However, this high accuracy …

Large models such as GPT-3 and ChatGPT have transformed deep learning (DL), powering
applications that have captured the public's imagination. Such models must be trained on …

Deep Learning system architects strive to design a balanced system where the
computational accelerator--FPGA, GPU, etc, is not starved for data. Feeding training data …

In the era of big data and cloud computing, large amounts of data are generated from user
applications and need to be processed in the datacenter. Data-parallel computing …

The proliferation of extensive neural network architectures, particularly deep learning
models, presents a challenge in terms of resource-intensive training. GPU memory …

Modern deep learning (DL) training is memory-consuming, constrained by the memory
capacity of each computation component and cross-device communication bandwidth. In …

Multi-tenant inference, as a prevalent inference paradigm nowadays, requires deploying
multiple deep learning models on the hardware platform to concurrently process inference …