4 citations · 7 across the 3 of their papers we have counts for
3 papers
cs.CV2023★ 1 cited
FLIQS: One-Shot Mixed-Precision Floating-Point and Integer Quantization Search
Jordan Dotzel, Gang Wu, Andrew Li +9
Quantization has become a mainstream compression technique for reducing model size, computational requirements, and energy consumption for modern deep neural networks (DNNs). With…
cs.CR2020★ 4 cited
GuardNN: Secure Accelerator Architecture for Privacy-Preserving Deep Learning
Weizhe Hua, Muhammad Umar, Zhiru Zhang +1
This paper proposes GuardNN, a secure DNN accelerator that provides hardware-based protection for user data and model parameters even in an untrusted environment. GuardNN shows tha…
cs.CR2020★ 2 cited
MGX: Near-Zero Overhead Memory Protection for Data-Intensive Accelerators
Weizhe Hua, Muhammad Umar, Zhiru Zhang +1
This paper introduces MGX, a near-zero overhead memory protection scheme for hardware accelerators. MGX minimizes the performance overhead of off-chip memory encryption and integri…