97 citations · 193 across the 8 of their papers we have counts for
10 papers
Lord of the Ring(s): Side Channel Attacks on the CPU On-Chip Ring Interconnect Are Practical
Riccardo Paccagnella, Licheng Luo, Christopher W. Fletcher
We introduce the first microarchitectural side channel attacks that leverage contention on the CPU ring interconnect. There are two challenges that make it uniquely difficult to ex…
Mind Mappings: Enabling Efficient Algorithm-Accelerator Mapping Space Search
Kartik Hegde, Po-An Tsai, Sitao Huang +3
Modern day computing increasingly relies on specialization to satiate growing performance and efficiency requirements. A core challenge in designing such specialized hardware archi…
DOVE: A Data-Oblivious Virtual Environment
Hyun Bin Lee, Tushar M. Jois, Christopher W. Fletcher +1
Users can improve the security of remote communications by using Trusted Execution Environments (TEEs) to protect against direct introspection and tampering of sensitive data. This…
Safer Illinois and RokWall: Privacy Preserving University Health Apps for COVID-19
Vikram Sharma Mailthody, James Wei, Nicholas Chen +14
COVID-19 has fundamentally disrupted the way we live. Government bodies, universities, and companies worldwide are rapidly developing technologies to combat the COVID-19 pandemic a…
Speculative Interference Attacks: Breaking Invisible Speculation Schemes
Mohammad Behnia, Prateek Sahu, Riccardo Paccagnella +13
Recent security vulnerabilities that target speculative execution (e.g., Spectre) present a significant challenge for processor design. The highly publicized vulnerability uses spe…
HarDNN: Feature Map Vulnerability Evaluation in CNNs
Abdulrahman Mahmoud, Siva Kumar Sastry Hari, Christopher W. Fletcher +7
As Convolutional Neural Networks (CNNs) are increasingly being employed in safety-critical applications, it is important that they behave reliably in the face of hardware errors. T…
SparseTrain:Leveraging Dynamic Sparsity in Training DNNs on General-Purpose SIMD Processors
Zhangxiaowen Gong, Houxiang Ji, Christopher Fletcher +2
Our community has greatly improved the efficiency of deep learning applications, including by exploiting sparsity in inputs. Most of that work, though, is for inference, where weig…
Morph: Flexible Acceleration for 3D CNN-based Video Understanding
Kartik Hegde, Rohit Agrawal, Yulun Yao +1
The past several years have seen both an explosion in the use of Convolutional Neural Networks (CNNs) and the design of accelerators to make CNN inference practical. In the archite…
Cache Telepathy: Leveraging Shared Resource Attacks to Learn DNN Architectures
Mengjia Yan, Christopher Fletcher, Josep Torrellas
Deep Neural Networks (DNNs) are fast becoming ubiquitous for their ability to attain good accuracy in various machine learning tasks. A DNN's architecture (i.e., its hyper-paramete…
UCNN: Exploiting Computational Reuse in Deep Neural Networks via Weight Repetition
Kartik Hegde, Jiyong Yu, Rohit Agrawal +3
Convolutional Neural Networks (CNNs) have begun to permeate all corners of electronic society (from voice recognition to scene generation) due to their high accuracy and machine ef…