Abstract Machine learning research has long focused on models rather than datasets, and
prominent datasets are used for common ML tasks without regard to the breadth, difficulty …

The predominant paradigm for using machine learning models on a device is to train a
model in the cloud and perform inference using the trained model on the device. However …

Data parallel training is widely used for scaling distributed deep neural network (DNN)
training. However, the performance benefits are often limited by the communication-heavy …

Modern deep neural networks (DNNs) training typically relies on GPUs to train complex
hundred-layer deep networks. A significant problem facing both researchers and industry …

Researchers have proposed hardware, software, and algorithmic optimizations to improve
the computational performance of deep learning. While some of these optimizations perform …

Distributed deep neural network (DDNN) training constitutes an increasingly important
workload that frequently runs in the cloud. Larger DNN models and faster compute engines …

The efficiency of Deep Learning (DL) training jobs is critically dependent on dataloaders,
which facilitate the transfer of data from storage to DL-accelerated hardware during training …

Deep Learning (DL) has achieved remarkable progress over the last decade on various
tasks such as image recognition, speech recognition, and natural language processing. In …

We introduce Deep500: the first customizable benchmarking infrastructure that enables fair
comparison of the plethora of deep learning frameworks, algorithms, libraries, and …

Background In deep learning the most significant breakthrough in the field of image
recognition, object detection language processing was done by Convolutional Neural …