Search Results for author: Jared Roesch

Found 7 papers, 2 papers with code

Bring Your Own Codegen to Deep Learning Compiler

no code implementations3 May 2021 Zhi Chen, Cody Hao Yu, Trevor Morris, Jorn Tuyls, Yi-Hsiang Lai, Jared Roesch, Elliott Delaye, Vin Sharma, Yida Wang

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.

Code Generation

Dynamic Tensor Rematerialization

1 code implementation ICLR 2021 Marisa Kirisame, Steven Lyubomirsky, Altan Haan, Jennifer Brennan, Mike He, Jared Roesch, Tianqi Chen, Zachary Tatlock

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

no code implementations4 Jun 2020 Haichen Shen, Jared Roesch, Zhi Chen, Wei Chen, Yong Wu, Mu Li, Vin Sharma, Zachary Tatlock, Yida Wang

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

no code implementations17 Apr 2019 Jared Roesch, Steven Lyubomirsky, Marisa Kirisame, Logan Weber, Josh Pollock, Luis Vega, Ziheng Jiang, Tianqi Chen, Thierry Moreau, Zachary Tatlock

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

1 code implementation10 Apr 2019 Eunice Jun, Maureen Daum, Jared Roesch, Sarah E. Chasins, Emery D. Berger, Rene Just, Katharina Reinecke

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

A Hardware-Software Blueprint for Flexible Deep Learning Specialization

no code implementations11 Jul 2018 Thierry Moreau, Tianqi Chen, Luis Vega, Jared Roesch, Eddie Yan, Lianmin Zheng, Josh Fromm, Ziheng Jiang, Luis Ceze, Carlos Guestrin, Arvind Krishnamurthy

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.

Code Generation Style Transfer

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