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Record W1943258332 · doi:10.1109/dft.2015.7315147

Enhancing embedded SRAM security and error tolerance with hardware CRC and obfuscation

2015· article· en· W1943258332 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicPhysical Unclonable Functions (PUFs) and Hardware Security
Canadian institutionsAdvanced Micro Devices (Canada)
Fundersnot available
KeywordsStatic random-access memoryComputer scienceScalabilityEmbedded systemObfuscationReliability (semiconductor)Soft errorHardware security moduleTrojanError detection and correctionComputer hardwareComputer securityOperating systemCryptographyEngineeringElectronic engineeringAlgorithm

Abstract

fetched live from OpenAlex

This paper proposes a scalable solution for obstructing and detecting malicious activity as well as erroneous events during mission mode operation of untrusted memories. The approach obfuscates data written into a memory and remaps the location of memory contents in a manner difficult for an attacker to predict, making it harder for a Hardware Trojan to be deterministically triggered or controlled by malicious agents. Simultaneously, the approach aids in the detection of soft errors. To our knowledge, this approach is among the first to reconcile SRAM security with SRAM soft error reliability. Simulation data gathered from a production-worthy silicon development environment confirms the viability of our method.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.628
Threshold uncertainty score0.524

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.016
GPT teacher head0.236
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it