FLIQS: One-Shot Mixed-Precision Floating-Point and Integer Quantization Search

no code implementations • 7 Aug 2023 • Jordan Dotzel, Gang Wu, Andrew Li, Muhammad Umar, Yun Ni, Mohamed S. Abdelfattah, Zhiru Zhang, Liqun Cheng, Martin G. Dixon, Norman P. Jouppi, Quoc V. Le, Sheng Li

With the proposed integer quantization search, we increase the accuracy of ResNet-18 on ImageNet by 1. 31% points and ResNet-50 by 0. 90% points with equivalent model cost over previous methods.

Quantization

GuardNN: Secure Accelerator Architecture for Privacy-Preserving Deep Learning

no code implementations • 26 Aug 2020 • Weizhe Hua, Muhammad Umar, Zhiru Zhang, G. Edward Suh

This paper proposes GuardNN, a secure DNN accelerator that provides hardware-based protection for user data and model parameters even in an untrusted environment.

Privacy Preserving Privacy Preserving Deep Learning

MGX: Near-Zero Overhead Memory Protection for Data-Intensive Accelerators

no code implementations • 20 Apr 2020 • Weizhe Hua, Muhammad Umar, Zhiru Zhang, G. Edward Suh

This paper introduces MGX, a near-zero overhead memory protection scheme for hardware accelerators.