In the past decade, Graphics Processing Units have played an important role in the field of
high-performance computing and they still advance new fields such as IoT, autonomous …

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

Graph neural networks (GNNs) are gaining popularity as a promising approach to machine
learning on graphs. Unlike traditional graph workloads where each vertex/edge is …

Modern deep neural networks increasingly make use of features such as control flow,
dynamic data structures, and dynamic tensor shapes. Existing deep learning systems focus …

It is promising to deploy CNN inference on local end-user devices for high-accuracy and
time-sensitive applications. Model parallelism has the potential to provide high throughput …

Our community has improved the efficiency of deep learning applications by exploiting
sparsity in inputs. Most of that work, though, is for inference, where weight sparsity is known …

The increasing adoption of smart systems in our daily life has led to the development of new
applications with varying performance and energy constraints, and suitable computing …

A growing number of applications implement predictive functions using deep learning
models, which require heavy use of compute and memory. One popular technique for …

This paper presents GoldenEye, a functional simulator with fault injection capabilities for
common and emerging numerical formats, implemented for the PyTorch deep learning …

It may be desirable to execute deep learning model inferences on an integrated GPU at the
edge. While such GPUs are much less powerful than discrete GPUs, it is able to deliver …