Revealing GPUs Vulnerabilities by Combining Register-Transfer and Software-Level Fault Injection

Author(s):  
Fernando F. dos Santos ◽  
Josie E. Rodriguez Condia ◽  
Luigi Carro ◽  
Matteo Sonza Reorda ◽  
Paolo Rech
2021 ◽  
Vol 17 (3) ◽  
pp. 1-24
Author(s):  
J. Laurent ◽  
C. Deleuze ◽  
F. Pebay-Peyroula ◽  
V. Beroulle

Protecting programs against hardware fault injection requires accurate software fault models. However, typical models, such as the instruction skip, do not take into account the microarchitecture specificities of a processor. We propose in this article an approach to study the relation between faults at the Register Transfer Level (RTL) and faults at the software level. The goal is twofold: accurately model RTL faults at the software level and materialize software fault models to actual RTL injections. These goals lead to a better understanding of a system's security against hardware fault injection, which is important to design effective and cost-efficient countermeasures. Our approach is based on the comparison between results from RTL simulations and software injections (using a program mutation tool). Various analyses are included in this article to give insight on the relevance of software fault models, such as the computation of a coverage and fidelity metric, and to link software fault models to hardware RTL descriptions. These analyses are applied on various single-bit and multiple-bit injection campaigns to study the faulty behaviors of a RISC-V processor.


Author(s):  
Rommel Estores ◽  
Karo Vander Gucht

Abstract This paper discusses a creative manual diagnosis approach, a complementary technique that provides the possibility to extend Automatic Test Pattern Generation (ATPG) beyond its own limits. The authors will discuss this approach in detail using an actual case – a test coverage issue where user-generated ATPG patterns and the resulting ATPG diagnosis isolated the fault to a small part of the digital core. However, traditional fault localization techniques was unable to isolate the fault further. Using the defect candidates from ATPG diagnosis as a starting point, manual diagnosis through fault Injection and fault simulation was performed. Further fault localization was performed using the ‘not detected’ (ND) and/or ‘detected’ (DT) fault classes for each of the available patterns. The result has successfully deduced the defect candidates until the exact faulty net causing the electrical failure was identified. The ability of the FA lab to maximize the use of ATPG in combination with other tools/techniques to investigate failures in detail; is crucial in the fast root cause determination and, in case of a test coverage, aid in having effective test screen method implemented.


Author(s):  
T. Kiyan ◽  
C. Boit ◽  
C. Brillert

Abstract In this paper, a methodology based upon laser stimulation and a comparison of continuous wave and pulsed laser operation will be presented that localizes the fault relevant sites in a fully functional scan chain cell. The technique uses a laser incident from the backside to inject soft faults into internal nodes of a master-slave scan flip-flop in consequence of localized photocurrent. Depending on the illuminated type of the transistors (n- or p-type), injection of a logic ‘0’ or ‘1’ into the master or the slave stage of a flip-flop takes place. The laser pulse is externally triggered and can easily be shifted to various time slots in reference to clock and scan pattern. This feature of the laser diode allows triggering the laser pulse on the rising or the falling edge of the clock. Therefore, it is possible to choose the stage of the flip-flop in which the fault injection should occur. It is also demonstrated that the technique is able to identify the most sensitive signal condition for fault injection with a better time resolution than the pulse width of the laser, a significant improvement for failure analysis of integrated circuits.


2021 ◽  
Vol 120 ◽  
pp. 114116
Author(s):  
Xiaolu Hou ◽  
Jakub Breier ◽  
Dirmanto Jap ◽  
Lei Ma ◽  
Shivam Bhasin ◽  
...  

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