RISC-V is an open-source instruction-set architecture (ISA) forming the basis of thousands of commercial and experimental microprocessors. The Scalar Cryptography extension ratified in December 2021 added scalar instructions that target common hashing and encryption algorithms, including SHA2 and AES. The next step forward for the RISC-V ISA in the field of cryptography and digital security is the development of vector cryptography instructions.
This thesis examines if it is viable to add vector implementations of existing RISC-V scalar cryptography instructions to the existing vector instruction format, and what improvements they can make to the execution of SHA2 and AES algorithms. Vector cryptography instructions vaeses, vaesesm, vaesds, vaesdsm, vsha256sch, and vsha256hash are proposed to optimize AES encryption and decryption, SHA256 message scheduling, and SHA256 hash rounds, with pseudocode, assembly examples, and a full 32-bit instruction format for each. Both algorithms stand to benefit greatly from vector instructions in reduction of computation time, code length, and instruction memory utilization due to large operand sizes and frequently repeated functions. As a proof of concept for the vector cryptography operations proposed, a full vector-based AES-128 encryption and SHA256 message schedule generation are performed on the 32-bit RISC-V Ibex processor and 128-bit Vicuna Vector Coprocessor in the Vivado simulation environment. Not counting stores or loads for fair comparison, the new Vector Cryptography extension completes a full encryption round in a single instruction compared to sixteen with the scalar extension, and can generate eight SHA256 message schedule double-words in a single instruction compared to the forty necessary on the scalar extension. These represent a 93.75% and 97.5% reduction in required instructions and memory for these functions respectively, at a hardware cost of 19.4% more LUTs and 1.44% more flip-flops on the edited Vicuna processor compared to the original.
| Identifer | oai:union.ndltd.org:CALPOLY/oai:digitalcommons.calpoly.edu:theses-4072 |
| Date | 01 June 2022 |
| Creators | Skelly, Jonathan W |
| Publisher | DigitalCommons@CalPoly |
| Source Sets | California Polytechnic State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Master's Theses |
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