18 citations · 30 across the 8 of their papers we have counts for
9 papers
Characterizing Concurrency Mechanisms for NVIDIA GPUs under Deep Learning Workloads
Guin Gilman, Robert J. Walls
We investigate the performance of the concurrency mechanisms available on NVIDIA's new Ampere GPU microarchitecture under deep learning training and inference workloads. In contras…
Memory-Efficient Deep Learning Inference in Trusted Execution Environments
Jean-Baptiste Truong, William Gallagher, Tian Guo +1
This study identifies and proposes techniques to alleviate two key bottlenecks to executing deep neural networks in trusted execution environments (TEEs): page thrashing during the…
Data-Free Model Extraction
Jean-Baptiste Truong, Pratyush Maini, Robert J. Walls +1
Current model extraction attacks assume that the adversary has access to a surrogate dataset with characteristics similar to the proprietary data used to train the victim model. Th…
Characterizing and Modeling Distributed Training with Transient Cloud GPU Servers
Shijian Li, Robert J. Walls, Tian Guo
Cloud GPU servers have become the de facto way for deep learning practitioners to train complex models on large-scale datasets. However, it is challenging to determine the appropri…
DRAB-LOCUS: An Area-Efficient AES Architecture for Hardware Accelerator Co-Location on FPGAs
Jacob T. Grycel, Robert J. Walls
Advanced Encryption Standard (AES) implementations on Field Programmable Gate Arrays (FPGA) commonly focus on maximizing throughput at the cost of utilizing high volumes of FPGA sl…
Silhouette: Efficient Protected Shadow Stacks for Embedded Systems
Jie Zhou, Yufei Du, Zhuojia Shen +3
Microcontroller-based embedded systems are increasingly used for applications that can have serious and immediate consequences if compromised---including automobile control systems…
Confidential Deep Learning: Executing Proprietary Models on Untrusted Devices
Peter M. VanNostrand, Ioannis Kyriazis, Michelle Cheng +2
Performing deep learning on end-user devices provides fast offline inference results and can help protect the user's privacy. However, running models on untrusted client devices re…
ERHARD-RNG: A Random Number Generator Built from Repurposed Hardware in Embedded Systems
Jacob Grycel, Robert J. Walls
Quality randomness is fundamental to cryptographic operations but on embedded systems good sources are (seemingly) hard to find. Rather than use expensive custom hardware, our ERHA…
Speeding up Deep Learning with Transient Servers
Shijian Li, Robert J. Walls, Lijie Xu +1
Distributed training frameworks, like TensorFlow, have been proposed as a means to reduce the training time of deep learning models by using a cluster of GPU servers. While such sp…