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

1 code implementation • 12 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.

Automating Generation of Low Precision Deep Learning Operators

no code implementations • 25 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.

Analysis and Mitigations of Reverse Engineering Attacks on Local Feature Descriptors

no code implementations • 9 May 2021 • Deeksha Dangwal, Vincent T. Lee, Hyo Jin Kim, Tianwei Shen, Meghan Cowan, Rajvi Shah, Caroline Trippel, Brandon Reagen, Timothy Sherwood, Vasileios Balntas, Armin Alaghi, Eddy Ilg

This poses a potential risk to user privacy.

Autonomous Driving

TACCL: Guiding Collective Algorithm Synthesis using Communication Sketches

2 code implementations • 8 Nov 2021 • Aashaka Shah, Vijay Chidambaram, Meghan Cowan, Saeed Maleki, Madan Musuvathi, Todd Mytkowicz, Jacob Nelson, Olli Saarikivi, Rachee Singh

TACCL uses a novel communication sketch abstraction to get crucial information from the designer to significantly reduce the search space and guide the synthesizer towards better algorithms.