Improving Dependability with Low Power Fault Detection Model for Skinny-Hash
Author Information
Author(s): Arvind Barge, Mary Gerardine
Primary Institution: School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
Hypothesis
Can low power fault detection techniques improve the dependability of the SKINNY-Hash function?
Conclusion
The proposed fault detection techniques achieve approximately 100% fault coverage with reduced area and power consumption compared to existing methods.
Supporting Evidence
- The proposed techniques were tested against various fault types and achieved high fault coverage.
- Results showed that the proposed methods have lower area and power overhead compared to existing techniques.
- Functional behavior was verified using ModelSim SE-64.
- The architectures were synthesized using the Genus synthesis tool by Cadence.
Takeaway
This study shows how to make devices that use the Internet of Things safer by finding and fixing problems without using too much power.
Methodology
The study implemented two fault detection techniques, RECO and DMRC, using VHDL and synthesized them in a 90nm CMOS process technology.
Limitations
The study does not address fault detection for all possible fault types and focuses primarily on transient and permanent faults.
Digital Object Identifier (DOI)
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