Deep Neural Networks (DNNs) are becoming an important tool in modern computing
applications. Accelerating their training is a major challenge and techniques range from …

The modern trend of moving artificial intelligence computation near to the origin of data
sources has increased the demand for new hardware and software suitable for such …

Training deep learning models is compute-intensive and there is an industry-wide trend
towards hardware specialization to improve performance. To systematically benchmark …

Deep learning models with convolutional and recurrent networks are now ubiquitous and
analyze massive amounts of audio, image, video, text and graph data, with applications in …

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

Despite the great promise that machine learning has offered in many fields of medicine, it
has also raised concerns about potential biases and poor generalization across genders …

This paper introduces Tiramisu, a polyhedral framework designed to generate high
performance code for multiple platforms including multicores, GPUs, and distributed …

We present a new algorithm to automatically schedule Halide programs for high-
performance image processing and deep learning. We significantly improve upon the …

This paper presents the design of Glow, a machine learning compiler for heterogeneous
hardware. It is a pragmatic approach to compilation that enables the generation of highly …

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 …