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An Efficient and Reliable Lightweight PUF for IoT-based Applications

2021· article· en· W3178986308 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 institutionsBrandon University
Fundersnot available
KeywordsReliability (semiconductor)Field-programmable gate arrayEmbedded systemPhysical unclonable functionComputer scienceRing oscillatorExploitInternet of ThingsStandardizationHardware security moduleReliability engineeringCryptographyVoltageEngineeringElectrical engineeringPower (physics)Operating systemComputer security

Abstract

fetched live from OpenAlex

Silicon physical unclonable functions (sPUFs) exploit manufacturing process variations of semiconductor integrated circuits (ICs) to protect IoT-based devices from new cyberattacks. In this paper, a novel security technique, namely, an efficient lightweight configurable-based ring oscillator PUFs (c-ROPUFs)design, is proposed to enhance IoT-based reliability. The c-ROPUF is a low area design, mapped in a single CLB, and easy to implement on reconfigurable hardware. Data samples are collected under varying temperatures and supply voltage over a population of 30 Spartan-3E FPGA chips. The reliability ofc-ROPUF is identified and evaluated based on the standards of the International Organization for Standardization (ISO) in terms of reliability. With the application of the proposed 1-out-of-encoding algorithm, our results demonstrate that c-ROPUF shows magnified average reliability of 99.63% as compared to earlier PUF designs. Finally, the results also show that the c-ROPUFdesign is immune from accelerated aging impacts with no bitflip, leading to reliability issues.

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: Methods · Consensus signal: none
Teacher disagreement score0.958
Threshold uncertainty score0.362

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 communication0.0000.000
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 head0.009
GPT teacher head0.241
Teacher spread0.232 · 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