This paper introduces two extensions to the popular PyTorch machine learning framework,
TorchDynamo and TorchInductor, which implement the torch. compile feature released in …

Deploying deep learning models on various devices has become an important topic. The
wave of hardware specialization brings a diverse set of acceleration primitives for multi …

With the increasing size of DNN models and the growing discrepancy between compute
performance and memory bandwidth, fusing multiple layers together to reduce off-chip …

Machine learning models with various tensor operators are becoming ubiquitous in recent
years. There are two types of operators in machine learning: compute-intensive operators …

The increasing demand for improving deep learning model performance has led to a
paradigm shift in supporting low-precision computation to harness the robustness of deep …

Modern GPUs accelerate computations and data movements of multi-dimensional tensors in
hardware. However, expressing optimized tensor computations in software is extremely …

We introduce Mosaic, a sparse tensor algebra compiler that can bind tensor expressions to
external functions of other tensor algebra libraries and compilers. Users can extend Mosaic …

The deep learning (DL) compiler serves as a vital infrastructure component to enable the
deployment of deep neural networks on diverse hardware platforms such as mobile devices …

The DNN models are now pervasively used for various applications. Meanwhile, the
computing hardware has shifted towards heterogeneous system composed of various …

As deep learning models nowadays are widely adopted by both cloud services and edge
devices, reducing the latency of deep learning model inferences becomes crucial to provide …