Deep neural networks (DNNs) have been ubiquitously applied in many applications, and accelerators are emerged as an enabler to support the fast and efficient inference tasks of these applications.
Checkpointing enables the training of deep learning models under restricted memory budgets by freeing intermediate activations from memory and recomputing them on demand.
Modern deep neural networks increasingly make use of features such as dynamic control flow, data structures and dynamic tensor shapes.
Using these extension mechanisms, Relay supports a unified compiler that can target a variety of hardware platforms.
We show that Tea generally matches the choices of experts while automatically switching to non-parametric tests when parametric assumptions are not met.
Programming Languages Human-Computer Interaction Mathematical Software Software Engineering
Machine learning powers diverse services in industry including search, translation, recommendation systems, and security.
Specialized Deep Learning (DL) acceleration stacks, designed for a specific set of frameworks, model architectures, operators, and data types, offer the allure of high performance while sacrificing flexibility.