Deep learning has recently become very popular on account of its incredible success in
many complex datadriven applications, including image classification and speech …

The emerging field of artificial intelligence of things (AIoT, AI+ IoT) is driven by the
widespread use of intelligent infrastructures and the impressive success of deep learning …

AI is undergoing a paradigm shift with the rise of models (eg, BERT, DALL-E, GPT-3) that are
trained on broad data at scale and are adaptable to a wide range of downstream tasks. We …

Existing deep neural network (DNN) frameworks optimize the computation graph of a DNN
by applying graph transformations manually designed by human experts. This approach …

Transformers are one of the most important machine learning workloads today. Training one
is a very compute-intensive task, often taking days or weeks, and significant attention has …

The popularity of Convolutional Neural Network (CNN) models and the ubiquity of CPUs
imply that better performance of CNN model inference on CPUs can deliver significant gain …

We study fusion for deep neural networks (DNNs) in a just-in-time (JIT) compilation
framework Apollo. It considers both memory-and compute-bound tensor operators for fusion …

Graph Neural Network (GNN) has recently drawn a rapid increase of interest in many
domains for its effectiveness in learning over graphs. Maximizing its performance is …

High-performance tensor programs are critical for efficiently deploying deep neural network
(DNN) models in real-world tasks. Existing frameworks optimize tensor programs by …

Existing quantum compilers optimize quantum circuits by applying circuit transformations
designed by experts. This approach requires significant manual effort to design and …