awslabs/s2n-bignum
Jun 2025 – Jun 2025
1 Month active
Languages Used
ML
Technical Skills
ARM ArchitectureCo-simulationCompiler DesignCompiler DevelopmentFormal VerificationReverse Engineering
amanda-zx
PROFILE
Amanda-zx
Worked on expanding ARM model coverage in the awslabs/s2n-bignum repository, focusing on instruction set completeness and emulator fidelity. Delivered support for the REV instruction for both 32-bit and 64-bit registers, integrating decoder patterns and operation logic to handle byte reversal. Added LDURB, STURB, LDUR, and STUR instructions with decoding logic and co-simulation tests, improving accuracy for 32-bit and 64-bit data operations. Employed skills in ARM Architecture, co-simulation, and compiler development using ML, with an emphasis on formal verification. Enhanced test automation and validation workflows, reducing edge-case risk in cryptographic workloads and strengthening overall reliability.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
2
Lines of code
51
Activity Months1
Your Network
1623 peopleWork History
June 2025
2 Commits • 2 Features
Jun 1, 2025June 2025: Expanded the ARM model coverage in awslabs/s2n-bignum with two key feature-focused deliveries. Implemented REV instruction support for both 32-bit and 64-bit registers, and added LDURB, STURB, LDUR, and STUR (64-bit and 32-bit) with decoding logic and co-simulation tests. These changes increase instruction-set completeness, improve emulator fidelity, and reduce edge-case risk in cryptographic workloads. The work demonstrates strong ARM decoding, instruction modeling, test automation, and collaboration across the project.
2 Commits • 2 Features
Jun 1, 2025June 2025: Expanded the ARM model coverage in awslabs/s2n-bignum with two key feature-focused deliveries. Implemented REV instruction support for both 32-bit and 64-bit registers, and added LDURB, STURB, LDUR, and STUR (64-bit and 32-bit) with decoding logic and co-simulation tests. These changes increase instruction-set completeness, improve emulator fidelity, and reduce edge-case risk in cryptographic workloads. The work demonstrates strong ARM decoding, instruction modeling, test automation, and collaboration across the project.
June 2025
Activity
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Quality Metrics
Correctness100.0%
Maintainability90.0%
Architecture90.0%
Performance90.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
ML
Technical Skills
ARM ArchitectureCo-simulationCompiler DesignCompiler DevelopmentFormal VerificationReverse Engineering
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