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Yudan Bai

dblp:389/6113 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2025
0009-0003-0372-1137ORCID · reported

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

Systems, architecture and hardware · 1 · 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% Processor architecture and microarchitecture · 19% Integrated circuit design · 19%
Network and information security
1 paper
Cryptographic primitives and cryptanalysis · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Cryptographic primitives and cryptanalysis
post-quantum cryptography
0.912025
PQNTRU: Acceleration of NTRU-Based Schemes via Customized Post-Quantum Processor · IEEE Trans. Computers 2025
Hardware accelerators and domain-specific architectures
cryptographic accelerator
0.912025
PQNTRU: Acceleration of NTRU-Based Schemes via Customized Post-Quantum Processor · IEEE Trans. Computers 2025
Processor architecture and microarchitecture › SIMD
SIMD processor
0.312025
PQNTRU: Acceleration of NTRU-Based Schemes via Customized Post-Quantum Processor · IEEE Trans. Computers 2025
Integrated circuit design
system-on-chip
0.312025
PQNTRU: Acceleration of NTRU-Based Schemes via Customized Post-Quantum Processor · IEEE Trans. Computers 2025

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

signed montgomery · 1.7layer merging · 1.7improved plantard · 1.7fast fourier transform · 1.7number-theoretic transform · 0.9number theoretic transform · 0.9
YearPublicationVenuePosition
2025 PQNTRU: Acceleration of NTRU-Based Schemes via Customized Post-Quantum Processor
abstract
Post-quantum cryptography (PQC) has rapidly evolved in response to the emergence of quantum computers, with the US National Institute of Standards and Technology (NIST) selecting four finalist algorithms for PQC standardization in 2022, including the Falcon digital signature scheme. Hawk is currently the only lattice-based candidate in NIST Round 2 additional signatures. Falcon and Hawk are based on the NTRU lattice, offering compact signatures, fast generation, and verification suitable for deployment on resource-constrained Internet-of-Things (IoT) devices. Despite the popularity of ML-DSA and ML-KEM, research on NTRU-based schemes has been limited due to their complex algorithms and operations. Falcon and Hawk's performance remains constrained by the lack of parallel execution in crucial operations like the Number Theoretic Transform (NTT) and Fast Fourier Transform (FFT), with data dependency being a significant bottleneck. This paper enhances NTRU-based schemes Falcon and Hawk through hardware/software co-design on a customized Single-Instruction-Multiple-Data (SIMD) processor, proposing new SIMD hardware units and instructions to expedite these schemes along with software optimizations to boost performance. Our NTT optimization includes a novel layer merging technique for SIMD architecture to reduce memory accesses, and the use of modular algorithms (Signed Montgomery and Improved Plantard) targets various modulus data widths to enhance performance. We explore applying layer merging to accelerate fixed-point FFT at the SIMD instruction level and devise a dual-issue parser to streamline assembly code organization to maximize dual-issue utilization. A System-on-chip (SoC) architecture is devised to improve the practical application of the processor in real-world scenarios. Evaluation on 28$nm$technology and field programmable gate array (FPGA) platform shows that our design and optimizations can increase the performance of Hawk signature generation and verification by over 7$\times$.
Zewen Ye, Junhao Huang 0001, Tianshun Huang, Yudan Bai, Guangyan Li, Donald Donglong Chen, Ray C. C. Cheung, Kejie Huang
IEEE Trans. Computers4