Abstract

Emerging byte-addressable Non-Volatile Memory (NVM) technology, although promising superior memory density and ultra-low energy consumption, poses unique challenges to achieving persistent data privacy and computing security, both of which are critically important to the embedded and IoT applications. Specifically, to successfully restore NVMs to their working states after unexpected system crashes or power failure, maintaining and recovering all the necessary security-related metadata can severely increase memory traffic, degrade runtime performance, exacerbate write endurance problem, and demand costly hardware changes to off-the-shelf processors. In this thesis, we summarize and expand upon two of our innovative works, ARES and HERMES, to design a new FPGA-assisted processor-transparent security mechanism aiming at efficiently and effectively achieving all three aspects of a security triad—confidentiality, integrity, and recoverability—in modern embedded computing. Given the growing prominence of CPU-FPGA heterogeneous computing architectures, ARES leverages FPGA's hardware reconfigurability to offload performance-critical and security-related functions to the programmable hardware without microprocessors' involvement. In particular, recognizing that the traditional Merkle tree caching scheme cannot fully exploit FPGA's parallelism due to its sequential and recursive function calls, ARES proposed a new Merkle tree cache architecture and a novel Merkle tree scheme which flattened and reorganized the computation in the traditional Merkle tree verification and update processes to fully exploit the parallel cache ports and to fully pipeline time-consuming hashing operations. To further optimize the throughput of BMT operations, HERMES proposed an optimally efficient dataflow architecture by processing multiple outstanding counter requests simultaneously. Specifically, HERMES explored and addressed three technical challenges when exploiting task-level parallelism of BMT and proposed a speculative execution approach with both low latency and high throughput.

Notes

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Graduation Date

2021

Semester

Summer

Advisor

Lin, Mingjie

Degree

Doctor of Philosophy (Ph.D.)

College

College of Engineering and Computer Science

Department

Electrical and Computer Engineering

Degree Program

Computer Engineering

Format

application/pdf

Identifier

CFE0008763;DP0025494

URL

https://purls.library.ucf.edu/go/DP0025494

Language

English

Release Date

August 2021

Length of Campus-only Access

None

Access Status

Doctoral Dissertation (Open Access)

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