The difficulty of deploying various deep learning (DL) models on diverse DL hardware has
boosted the research and development of DL compilers in the community. Several DL …

With the recent trend of on-device deep learning, inference latency has become a crucial
metric in running Deep Neural Network (DNN) models on various mobile and edge devices …

In this paper, we present WACO, a novel method of co-optimizing the format and the
schedule of a given sparsity pattern in a sparse tensor program. A core challenge in this …

With the wide and deep adoption of deep learning models in real applications, there is an
increasing need to model and learn the representations of the neural networks themselves …

Machine learning (ML) has become a prevalent approach to tame the complexity of design
space exploration for domain-specific architectures. While appealing, using ML for design …

Deep neural networks (DNNs) are widely used in various applications. The accurate and
latency feedback is essential for model design and deployment. In this work, we attempt to …

Deep Neural Networks (DNNs) have shown excellent performance in a wide range of
machine learning applications. Knowing the latency of running a DNN model or tensor …

Unmanned aerial vehicles (UAVs) play an essential role in emergency cases and adverse
environments for applications like disaster detection and mine exploration. To process the …

In the early design phase of a Deep Neural Network (DNN) acceleration system, fast energy
and latency estimation are important to evaluate the optimality of different design candidates …

The recent advances of hardware-based accelerators for machine learning—in particular
neural networks—attracted the attention of embedded-systems designers and engineers …