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Found 7 papers, 2 papers with code
Bring Your Own Codegen to Deep Learning Compiler
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.
Dynamic Tensor Rematerialization
Checkpointing enables the training of deep learning models under restricted memory budgets by freeing intermediate activations from memory and recomputing them on demand.
Nimble: Efficiently Compiling Dynamic Neural Networks for Model Inference
Modern deep neural networks increasingly make use of features such as dynamic control flow, data structures and dynamic tensor shapes.
Relay: A High-Level Compiler for Deep Learning
Using these extension mechanisms, Relay supports a unified compiler that can target a variety of hardware platforms.
Tea: A High-level Language and Runtime System for Automating Statistical Analysis
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
Relay: A New IR for Machine Learning Frameworks
Machine learning powers diverse services in industry including search, translation, recommendation systems, and security.
A Hardware-Software Blueprint for Flexible Deep Learning Specialization
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.
