FPGA-based accelerators are increasingly popular across a broad range of applications,
because they offer massive parallelism, high energy efficiency, and great flexibility for …

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 …

This paper presents Timeloop, an infrastructure for evaluating and exploring the architecture
design space of deep neural network (DNN) accelerators. Timeloop uses a concise and …

Irregular applications such as big graph analysis, material simulations, molecular dynamics
simulations, and finite element analysis have performance problems due to their use of …

This book provides a structured treatment of the key principles and techniques for enabling
efficient processing of deep neural networks (DNNs). DNNs are currently widely used for …

Recent advances in state-of-the-art DNN architecture design have been moving toward
Transformer models. These models achieve superior accuracy across a wide range of …

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

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 …

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 …

Deep Neural Networks (DNNs) have emerged as the core enabler of many major
applications on mobile devices. To achieve high accuracy, DNN models have become …