November 2022 - April 2023
Fully Homomorphic Encryption (FHE) is an emerging technology that allows for computation on encrypted operands, making it an ideal solution for security in the cloud computing era. With the looming threat that quantum computing poses on once-trusted cryptographic schemes, lattice-based FHE schemes have the potential to provide post-quantum security against cryptanalytic attacks. Although modern FHE schemes offer unprecedented security, current implementations suffer from prohibitively high computational costs. In this research we leverage NaviSim, a GPU simulator that faithfully models AMD architectures, to demonstrate how microarchitectural changes can be made to accelerate the performance of FHE.
January 2022 - March 2023
In this project we are aiming to develop a methodology for automatically calibrating modern GPU simulators against actual GPUS. I have led this initiative by writing a script that can automatically calibrate single simulator parameters against suites of benchmarks. In addition, I structured navisim parameters to be configurable with the Opentuner python library for autotuning and automated the testing of validation microbenchmarks. Using this methodology we have been able to calibrate simulator parameters to be within 30% accurate of native hardware output.
June 2022 - December 2022
Recent infrastructure collapses, such as the MBTA’s Government Center collapse, have highlighted the importance of safe and efficient methods for evaluating critical infrastructure. To combat this issue, we have developed a small Unmanned Aerial System that can detect and evaluate the risks associated with hazardous fractures within tunnel walls. In our proposed solution we are leveraging Region-based Convolutional Neural Networks (R-CNN) for an applied mask in computer vision, Simultaneous Localization And Mapping (SLAM) for global navigation in GPS-denied tunnels, and an integrated sensor suite for visualizing and interpreting crack integrity while maintaining flight capabilities in remote environments. With these techniques, we have the capability to deploy a tool that provides insightful analysis on various civil infrastructure evaluations to expert civil engineers. With the developed system we hope to provide the industrial and academic communities with a prototyped system that can help mitigate the impact of cracks in vulnerable infrastructure.
November 2021 - November 2022
Security research targeting today’s highperformance CPU microarchitectures helps to insure that tomorrow’s program execution will be secure and reliable. With the adoption of branch predictors and speculative execution to overcome data and control dependencies on nearly every microprocessor on the market today, timing side channel attacks have become a critical issue. In this project we explore how different branch predictor designs, implemented on the SonicBOOM RISC-V architecture, can improve performance, but are also susceptible to side channels.
June 2020 - April 2022
As GPUs continue to grow in popularity for accelerating demanding applications, such as high-performance computing and machine learning, GPU architects need to deliver more powerful devices with updated instruction set architectures (ISAs) and new microarchitectural features. The introduction of the AMD RDNA architecture is one example where the GPU architecture was dramatically changed, modifying the underlying programming model, the core architecture, and the cache hierarchy. To date, no publicly-available simulator infrastructure can model the AMD RDNA GPU, preventing researchers from exploring new GPU designs based on the state-of-the-art RDNA architecture. In this project, we present the NaviSim simulator, the first cyclelevel GPU simulator framework that models AMD RDNA GPUs. NaviSim faithfully emulates the new RDNA ISA. We extensively tune and validate NaviSim using several microbenchmarks and 10 full workloads. Our evaluation shows that NaviSim can accurately model the GPU’s kernel execution time, achieving similar performance to hardware execution within 9.92% (on average), as measured on an AMD RX 5500 XT GPU and an AMD Radeon Pro W6800 GPU.
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