Customizing Trusted AI Accelerators for Efficient Privacy-Preserving Machine Learning

12 November 2020

Papers citing "Customizing Trusted AI Accelerators for Efficient Privacy-Preserving Machine Learning"

8 / 8 papers shown

Title
Slalom: Fast, Verifiable and Private Execution of Neural Networks in Trusted Hardware Florian Tramèr Dan Boneh FedML 161 396 0 08 Jun 2018
Chiron: Privacy-preserving Machine Learning as a Service T. Hunt Congzheng Song Reza Shokri Vitaly Shmatikov Emmett Witchel 35 201 0 15 Mar 2018
TVM: An Automated End-to-End Optimizing Compiler for Deep Learning Tianqi Chen T. Moreau Ziheng Jiang Lianmin Zheng Eddie Q. Yan ... Leyuan Wang Yuwei Hu Luis Ceze Carlos Guestrin Arvind Krishnamurthy 148 374 0 12 Feb 2018
Gazelle: A Low Latency Framework for Secure Neural Network Inference Chiraag Juvekar Vinod Vaikuntanathan A. Chandrakasan 46 889 0 16 Jan 2018
DeepSecure: Scalable Provably-Secure Deep Learning B. Rouhani M. Riazi F. Koushanfar FedML 50 413 0 24 May 2017
In-Datacenter Performance Analysis of a Tensor Processing Unit N. Jouppi C. Young Nishant Patil David Patterson Gaurav Agrawal ... Vijay Vasudevan Richard Walter Walter Wang Eric Wilcox Doe Hyun Yoon 207 4,626 0 16 Apr 2017
SGXIO: Generic Trusted I/O Path for Intel SGX Samuel Weiser M. Werner UQCV 47 95 0 04 Jan 2017
Deep Residual Learning for Image Recognition Kaiming He Xinming Zhang Shaoqing Ren Jian Sun MedIm 1.8K 193,426 0 10 Dec 2015