Search Results for author: Thierry Moreau

Found 7 papers, 2 papers with code

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.

Automating Generation of Low Precision Deep Learning Operators

no code implementations25 Oct 2018 Meghan Cowan, Thierry Moreau, Tianqi Chen, Luis Ceze

To date, none of the popular deep learning directly support low precision operators, partly due to a lack of optimized low precision libraries.

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

Learning to Optimize Tensor Programs

no code implementations NeurIPS 2018 Tianqi Chen, Lianmin Zheng, Eddie Yan, Ziheng Jiang, Thierry Moreau, Luis Ceze, Carlos Guestrin, Arvind Krishnamurthy

Efficient implementations of tensor operators, such as matrix multiplication and high dimensional convolution, are key enablers of effective deep learning systems.

TVM: An Automated End-to-End Optimizing Compiler for Deep Learning

1 code implementation12 Feb 2018 Tianqi Chen, Thierry Moreau, Ziheng Jiang, Lianmin Zheng, Eddie Yan, Meghan Cowan, Haichen Shen, Leyuan Wang, Yuwei Hu, Luis Ceze, Carlos Guestrin, Arvind Krishnamurthy

Experimental results show that TVM delivers performance across hardware back-ends that are competitive with state-of-the-art, hand-tuned libraries for low-power CPU, mobile GPU, and server-class GPUs.

Introducing ReQuEST: an Open Platform for Reproducible and Quality-Efficient Systems-ML Tournaments

7 code implementations19 Jan 2018 Thierry Moreau, Anton Lokhmotov, Grigori Fursin

Co-designing efficient machine learning based systems across the whole hardware/software stack to trade off speed, accuracy, energy and costs is becoming extremely complex and time consuming.

MATIC: Learning Around Errors for Efficient Low-Voltage Neural Network Accelerators

no code implementations14 Jun 2017 Sung Kim, Patrick Howe, Thierry Moreau, Armin Alaghi, Luis Ceze, Visvesh Sathe

As a result of the increasing demand for deep neural network (DNN)-based services, efforts to develop dedicated hardware accelerators for DNNs are growing rapidly.

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