Search Results for author:
Found 6 papers, 2 papers with code
Characterizing Concurrency Mechanisms for NVIDIA GPUs under Deep Learning Workloads
We investigate the performance of the concurrency mechanisms available on NVIDIA's new Ampere GPU microarchitecture under deep learning training and inference workloads.
Memory-Efficient Deep Learning Inference in Trusted Execution Environments
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 execution of convolutional layers and the decryption of large weight matrices in fully-connected layers.
Data-Free Model Extraction
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.
Characterizing and Modeling Distributed Training with Transient Cloud GPU Servers
However, it is challenging to determine the appropriate cluster configuration---e. g., server type and number---for different training workloads while balancing the trade-offs in training time, cost, and model accuracy.
Confidential Deep Learning: Executing Proprietary Models on Untrusted Devices
Performing deep learning on end-user devices provides fast offline inference results and can help protect the user's privacy.
Speeding up Deep Learning with Transient Servers
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.
