Deep learning has revolutionized the fields of computer vision, natural language
understanding, speech recognition, information retrieval, and more. However, with the …

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

There is an increasing need to bring machine learning to a wide diversity of hardware
devices. Current frameworks rely on vendor-specific operator libraries and optimize for a …

Current low-precision quantization algorithms often have the hidden cost of conversion back
and forth from floating point to quantized integer values. This hidden cost limits the latency …

We introduce a learning-based framework to optimize tensor programs for deep learning
workloads. Efficient implementations of tensor operators, such as matrix multiplication and …

With the emergence of a spectrum of high-end mobile devices, many applications that
formerly required desktop-level computation capability are being transferred to these …

High-performance tensor programs are crucial to guarantee efficient execution of deep
neural networks. However, obtaining performant tensor programs for different operators on …

3D visual computing data are often spatially sparse. To exploit such sparsity, people have
developed hierarchical sparse data structures, such as multi-level sparse voxel grids …

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

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 …