5 papers
Dynamic Tensor Rematerialization
Marisa Kirisame, Steven Lyubomirsky, Altan Haan +5
Checkpointing enables the training of deep learning models under restricted memory budgets by freeing intermediate activations from memory and recomputing them on demand. Current c…
Nimble: Efficiently Compiling Dynamic Neural Networks for Model Inference
Haichen Shen, Jared Roesch, Zhi Chen +6
Modern deep neural networks increasingly make use of features such as dynamic control flow, data structures and dynamic tensor shapes. Existing deep learning systems focus on optim…
Tea: A High-level Language and Runtime System for Automating Statistical Analysis
Eunice Jun, Maureen Daum, Jared Roesch +4
Though statistical analyses are centered on research questions and hypotheses, current statistical analysis tools are not. Users must first translate their hypotheses into specific…
Relay: A High-Level Compiler for Deep Learning
Jared Roesch, Steven Lyubomirsky, Marisa Kirisame +7
Frameworks for writing, compiling, and optimizing deep learning (DL) models have recently enabled progress in areas like computer vision and natural language processing. Extending…
Relay: A New IR for Machine Learning Frameworks
Jared Roesch, Steven Lyubomirsky, Logan Weber +4
Machine learning powers diverse services in industry including search, translation, recommendation systems, and security. The scale and importance of these models require that they…