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Zheng-Yan Wong

dblp:293/9525 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2023
—ORCID · unresolved

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
Hardware accelerators and domain-specific architectures · 62% Reconfigurable computing and FPGAs · 19% Integrated circuit design · 19%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Hardware accelerators and domain-specific architectures › cryptographic accelerator
post-quantum cryptography accelerator
0.712023
KaratSaber: New Speed Records for Saber Polynomial Multiplication Using Efficient Karatsuba FPGA Architecture · IEEE Trans. Computers 2023
Reconfigurable computing and FPGAs
FPGA arithmetic
0.212023
KaratSaber: New Speed Records for Saber Polynomial Multiplication Using Efficient Karatsuba FPGA Architecture · IEEE Trans. Computers 2023
Integrated circuit design › digital arithmetic circuits › integer multiplier
karatsuba multiplier
0.212023
KaratSaber: New Speed Records for Saber Polynomial Multiplication Using Efficient Karatsuba FPGA Architecture · IEEE Trans. Computers 2023

Methods — techniques the papers use, named apart from their topics

negacyclic convolution · 0.7karatsuba algorithm · 0.7instruction code result-mapping · 0.7
YearPublicationVenuePosition
2023 KaratSaber: New Speed Records for Saber Polynomial Multiplication Using Efficient Karatsuba FPGA Architecture
abstract
SABER is a round 3 candidate in the NIST Post-Quantum Cryptography Standardization process. Polynomial convolution is one of the most computationally intensive operation in Saber Key Encapsulation Mechanism, that can be performed through widely explored algorithms like the schoolbook polynomial multiplication algorithm (SPMA) and Number Theoretic Transform (NTT). While SPMA multiplier has a slow latency performance, the NTT-based multiplier usually requires large hardware. In this work, we propose KaratSaber, an optimized Karatsuba polynomial multiplier architecture with a balanced hardware efficiency (throughput-per-slice, TPS) compared to NTT and SPMA based designs. KaratSaber employs several techniques for an efficient design: a parallel grid input technique for efficient pre-processing stage in Karatsuba-based polynomial multiplier, a novel instruction code result-mapping technique catering the negacyclic operations improves the post-processing stage efficiency, a double multiplicand shifter-based multiplier doubles the throughput at the multiplication stage. Combining these three techniques, the proposed KaratSaber architecture is 7.47 × faster compared to the state-of-the-art SPMA Saber architecture at the expense of 4.96 × additional hardware resources; making KaratSaber 46.04% more area-time efficient. When compared to LWRPro, a recent Karatsuba Saber architecture, KaratSaber architecture achieves a 2.11 × higher throughput by only utilizing 1.92 × additional hardware; thus gaining a 10.44% improvement in area-time efficiency.
Zheng-Yan Wong, Denis Chee-Keong Wong, Wai-Kong Lee, Kai Ming Mok, Wun-She Yap, Ayesha Khalid
IEEE Trans. Computers1