SPHINCSLET: An Area-Efficient Accelerator for the Full SPHINCS+ Digital Signature Algorithm
This work presents SPHINCSLET, the first fully standard-compliant and area-efficient hardware implementation of the SLH-DSA algorithm, formerly known as SPHINCS+, a post-quantum digital signature scheme. SPHINCSLET is designed to be parameterizable across different security levels and hash functions, offering a balanced tradeoff between area efficiency and performance. Existing hardware implementations either feature a large area footprint to achieve fast signing and verification or adopt a coprocessor-based approach that significantly slows down these operations. SPHINCSLET addresses this gap by delivering a 4.7× reduction in area compared to high-speed designs while achieving a 2.5× to 5× improvement in signing time over the most efficient coprocessor-based designs for a SHAKE256-based SPHINCS+ implementation. The SHAKE256-based SPHINCS+ FPGA implementation targeting the AMD Artix-7 requires fewer than 10.8K LUTs for any security level of SLH-DSA. Furthermore, the SHA-2-based SPHINCS+ implementation achieves a 2× to 4× speedup in signature generation across various security levels compared to existing SLH-DSA hardware, all while maintaining a compact area footprint of 6K to 15K LUTs. This makes it the fastest SHA-2-based SLH-DSA implementation to date. With an optimized balance of area and performance, SPHINCSLET can assist resource-constrained devices in transitioning to post-quantum cryptography.
Authors
- Jakub Szefer (ORCID: https://orcid.org/0000-0001-9721-3640)
- Yunheung Paek (ORCID: https://orcid.org/0000-0002-6412-2926)
- Yong-Seok Lee (ORCID: https://orcid.org/0000-0002-9717-620X)
- Sanjay Deshpande (ORCID: https://orcid.org/0009-0005-0938-9547)
- Cansu Karakuzu
Institutions
- Northwestern University (US)
- Seoul National University (KR)
- Hasso Plattner Institute (DE)
- University of Potsdam (DE)
- Yale University (US)
Publication Details
- Journal
- ACM Transactions on Embedded Computing Systems
- Published
- 2025-04-07
- DOI
- https://doi.org/10.1145/3728469
- Citations
- 7
- Primary Topic
- Algorithms and Data Compression
- Type
- article
- Field-Weighted Citation Impact
- 17.37