Name
Title | ||
|---|---|---|
A 0.26% BER, 10<sup>28</sup> Challenge-Response Machine-Learning Resistant Strong-PUF in 14nm CMOS Featuring Stability-Aware Adversarial Challenge Selection |
Abstract | ||
|---|---|---|
A 10
<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">28</sup>
challenge-response strong-PUF in 14nm CMOS, demonstrates machine learning (ML) attack resistance across 6-million training samples. The 2-stage non-linear cascaded PUF array with adversarial challenge selection limits ML attack accuracy to ~50%. The configurable cross-coupled inverter-based entropy source with stability-aware challenge pruning enables 9.8× higher array density and 0.26% peak BER across 650-850mV and 0-100°C. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/VLSICircuits18222.2020.9162890 | 2020 IEEE Symposium on VLSI Circuits |
Keywords | DocType | ISSN |
higher array density,stability-aware challenge pruning,configurable cross-coupled inverter-based entropy source,ML attack accuracy,2-stage nonlinear cascaded PUF array,6-million training samples,machine learning,challenge-response strong-PUF,stability-aware adversarial challenge selection,14nm CMOS,challenge-response machine-learning resistant strong-PUF,0.26% BER,size 14.0 nm,voltage 650.0 mV to 850.0 mV,temperature 0.0 degC to 100.0 degC | Conference | 2158-5601 |
ISBN | Citations | PageRank |
978-1-7281-9943-6 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vikram B. Suresh | 1 | 2 | 1.73 |
| Raghavan Kumar | 2 | 73 | 12.56 |
| Mark Anders | 3 | 315 | 50.81 |
| Himanshu Kaul | 4 | 0 | 0.34 |
| Vivek De | 5 | 3024 | 577.83 |
| Sanu Mathew | 6 | 50 | 3.78 |