Abstract

Modern applications on general purpose processors require both rapid and power-efficient computing and memory components. As applications continue to improve, the demand for high speed computation, fast-access memory, and a secure platform increases. Traditional Von Neumann Architectures split the computing and memory units, causing both latency and high power-consumption issues; henceforth, a hybrid memory processing system is proposed, known as in-memory processing. In-memory processing alleviates the delay of computation and minimizes power-consumption; such improvements saw a 14x speedup improvement, 87\% fewer power consumption, and appropriate linear scalability versus performance. Several applications of in-memory processing include data-driven applications such as Artificial Intelligence (AI), Convolutional and Deep Neural Networks (CNNs/DNNs). However, processing-in-memory can also suffer from a security and reliability issue known as the Row Hammer Security Bug; this security exploit flips bits within memory without access, leading to error injection, system crashes, privilege separation, and total hijack of a system; the novel Row Hammer security bug can negatively impact the accuracies of CNNs and DNNs via flipping the bits of stored weight values without direct access. Weights of neural networks are stored in a variety of data patterns, resulting in either a solid (all 1s or all 0s), checkered (alternating 1s and 0s in both rows and columns), row-stripe (alternating 1s and 0s in rows), or column-striped (alternating 1s and 0s in columns) manner; the row-stripe data pattern exhibits the largest likelihood of a Row Hammer attack, resulting in the accuracies of neural networks dropping over 30\%. A row-stripe avoidance coding scheme is proposed to reduce the probability of the Row Hammer Attack occurring within neural networks. The coding scheme encodes the binary portion of a weight in a CNN or DNN to reduce the chance of row-stripe data patterns, overall reducing the likelihood of a Row Hammer attack occurring while improving the overall security of the in-memory processing system.

Library of Congress Subject Headings

Computer storage devices--Security measures; High performance processors--Security measures; Cyberterrorism--Prevention

Publication Date

6-2021

Document Type

Thesis

Student Type

Graduate

Degree Name

Computer Engineering (MS)

Department, Program, or Center

Computer Engineering (KGCOE)

Advisor

Amlan Ganguly

Advisor/Committee Member

Corey Merkel

Advisor/Committee Member

Mark Indovina

Campus

RIT – Main Campus

Plan Codes

CMPE-MS

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