arXiv subjects

Jieyu Zheng

Publications and source records attributed to Jieyu Zheng.

8 recordsLinked to original sources

arXiv2026

Exposing SIMD Parallelism in SQIsign: An AVX-512 Implementation

Modern isogeny-based cryptosystems spend much of their running time in finite-field, elliptic-curve, and higher-dimensional isogeny arithmetic. Exploiting SIMD parallelism is nontrivial: routines such as Montgomery ladders contain loop-carried dependencies, while point, pairing, and theta-coordinate formulas expose only irregular fine-grained parallelism. We show that substantial SIMD parallelism can be recovered by reorganizing the arithmetic dependency graphs of higher-level primitives rather than vectorizing field multiplication in isolation. We develop an end-to-end AVX-512IFMA implementation of SQIsign in which data remain in a radix-$2^{51}$ vector representation across most of the curve-side computation. Our redesign includes projective xDBLADD schedules, batched point doubling in several coordinate systems, a vectorized biscalar ladder, fused cubical-arithmetic pairing steps, and batched one- and two-dimensional isogeny evaluation. Relative to the reference C implementation, we achieve end-to-end speedups of $1.76\times$, $1.71\times$, and $3.18\times$ for key generation, signing, and verification at NIST level~I; combined with Qlapoti, key-generation and signing speedups rise to $2.90\times$ and $2.69\times$. We further apply the same backend and methodology to CORAL, a recent isogeny group action for post-quantum non-interactive key exchange based on two-dimensional $2$-isogenies. Across five parameter sets, this yields $1.28$--$1.40\times$ speedups for key generation and $1.92$--$2.46\times$ for shared-key computation. These results provide cross-scheme evidence that algorithm-level SIMD scheduling is a reusable optimization dimension for higher-dimensional isogeny cryptography.

cs.CR↗

arXiv2026

In-situ integrated nonlocal Huygens metasurfaces enable robust vortex beams from multimode fibers

A fundamental hurdle for multimode fiber (MMF) integrated photonics is achieving robust and deterministic wavefront shaping against fiber perturbation, which induces random modal interference and scrambles the real-space output into speckle patterns. Existing wavefront shaping methods typically rely on pixelated phase or polarization pre-compensation, rendering them highly sensitive to fiber deformation. Here, we show that in-situ integration of a silicon nonlocal metasurface onto an MMF end facet enables direct conversion of arbitrary speckle patterns into optical vortex beams without pre-compensation. The metasurface supports overlapped Mie resonances and nonlocal bound states in the continuum (BICs), yielding high-transmission bands by satisfying the generalized nonlocal Huygens condition. Through momentum-space coupling between MMF modes and Huygens-BICs with topological polarization-vortex nature, the output maintains doughnut-shaped profiles under static and dynamic perturbations, demonstrating exceptional robustness against fiber deformation. This approach provides a compact, alignment-tolerant platform for robust structured light generation in MMFs for endoscopy, optical trapping and communications.

physics.optics↗

arXiv2024

The Unbearable Slowness of Being: Why do we live at 10 bits/s?

This article is about the neural conundrum behind the slowness of human behavior. The information throughput of a human being is about 10 bits/s. In comparison, our sensory systems gather data at ~10^9 bits/s. The stark contrast between these numbers remains unexplained and touches on fundamental aspects of brain function: What neural substrate sets this speed limit on the pace of our existence? Why does the brain need billions of neurons to process 10 bits/s? Why can we only think about one thing at a time? The brain seems to operate in two distinct modes: the "outer" brain handles fast high-dimensional sensory and motor signals, whereas the "inner" brain processes the reduced few bits needed to control behavior. Plausible explanations exist for the large neuron numbers in the outer brain, but not for the inner brain, and we propose new research directions to remedy this.

q-bio.NC↗

arXiv2024

Faster Post-Quantum TLS 1.3 Based on ML-KEM: Implementation and Assessment

TLS is extensively utilized for secure data transmission over networks. However, with the advent of quantum computers, the security of TLS based on traditional public-key cryptography is under threat. To counter quantum threats, it is imperative to integrate post-quantum algorithms into TLS. Most PQ-TLS research focuses on integration and evaluation, but few studies address the improvement of PQ-TLS performance by optimizing PQC implementation. For the TLS protocol, handshake performance is crucial, and for post-quantum TLS (PQ-TLS) the performance of post-quantum key encapsulation mechanisms (KEMs) directly impacts handshake performance. In this work, we explore the impact of post-quantum KEMs on PQ-TLS performance. We explore how to improve ML-KEM performance using the latest Intel's Advanced Vector Extensions instruction set AVX-512. We detail a spectrum of techniques devised to parallelize polynomial multiplication, modular reduction, and other computationally intensive modules within ML-KEM. Our optimized ML-KEM implementation achieves up to 1.64x speedup compared to the latest AVX2 implementation. Furthermore, we introduce a novel batch key generation method for ML-KEM that can seamlessly integrate into the TLS protocols. The batch method accelerates the key generation procedure by 3.5x to 4.9x. We integrate the optimized AVX-512 implementation of ML-KEM into TLS 1.3, and assess handshake performance under both PQ-only and hybrid modes. The assessment demonstrates that our faster ML-KEM implementation results in a higher number of TLS 1.3 handshakes per second under both modes. Additionally, we revisit two IND-1-CCA KEM constructions discussed in Eurocrypt22 and Asiacrypt23. Besides, we implement them based on ML-KEM and integrate the one of better performance into TLS 1.3 with benchmarks.

cs.CR↗

arXiv2024

ESPM-D: Efficient Sparse Polynomial Multiplication for Dilithium on ARM Cortex-M4 and Apple M2

Dilithium is a lattice-based digital signature scheme standardized by the NIST post-quantum cryptography (PQC) project. In this study, we focus on developing efficient sparse polynomial multiplication implementations of Dilithium for ARM Cortex-M4 and Apple M2, which are both based on the ARM architecture. The ARM Cortex-M4 is commonly utilized in resource-constrained devices such as sensors. Conversely, the Apple M2 is typically found on mobile devices, emphasizing high performance and versatility. Accordingly, our optimization strategies differ between ARM Cortex-M4 and Apple M2. We prioritize optimizing stack usage for the former while enhancing computational efficiency for the latter. Our optimized sparse polynomial multiplication achieves significant speedups of up to 30% on ARM Cortex-M4 and 55% on Apple M2 compared to the state-of-the-art Number-Theoretic Transform (NTT) implementation. Additionally, we integrate the sparse polynomial multiplication with the infinity norm judgments in the Dilithium signing process, further enhancing signing efficiency. Our optimized implementation not only reduces stack usage by 10.8%, 1.2%, and 7.7% in the signing procedure of Dilithium2, Dilithium3, and Dilithium5, respectively, but also enhances signing performance by 0.4% to 0.8% compared to the state-of-the-art ARM Cortex-M4 implementation. Furthermore, we optimize polynomial sampling, rounding functions, and polynomial packing and unpacking using ARM Cortex-M4 DSP instructions, resulting in a 0.4%-3.2% improvement in key generation and verification procedures. On the MacBook Air 2022, our Dilithium implementation achieves 10% to 11% speedups in the signing procedure. To the best of our knowledge, our work sets new performance records for Dilithium on both ARM Cortex-M4 and Apple M2 platforms.

cs.CR↗

arXiv2023

Optimized Vectorization Implementation of CRYSTALS-Dilithium

CRYSTALS-Dilithium is a lattice-based signature scheme to be standardized by NIST as the primary post-quantum signature algorithm. In this work, we make a thorough study of optimizing the implementations of Dilithium by utilizing the Advanced Vector Extension (AVX) instructions, specifically AVX2 and the latest AVX-512. We first present an improved parallel small polynomial multiplication with tailored early evaluation (PSPM-TEE) to further speed up the signing procedure. Our PSPM algorithm outperform the NTT by 47%-66% in AVX2 and AVX-512 implementation. We then present a tailored reduction method that is simpler and faster than Montgomery reduction. We minimize the CPU cycles of tailored reduction AVX-512 implementation by using AVX-512IFMA. Finally, we propose a fully and highly vectorized implementation of Dilithium using AVX-512. This is achieved by carefully vectorizing most of Dilithium functions with the AVX-512 instructions in order to improve efficiency both for time and for space simultaneously. With all the optimization efforts, our AVX-512 implementation improves the performance by 43.2%/39.3%/45.6% in key generation, 36.6%/41.6%/43.7% in signing, and 45.3%/46.5%/47.4% in verification for the parameter sets of Dilithium2/3/5 respectively. To the best of our knowledge, our AVX-512 implementation has the best performance for Dilithium on the Intel x86-64 CPU platform to date.

cs.CR↗

arXiv2022

Compact and Efficient KEMs over NTRU Lattices

The NTRU lattice is a promising candidate to construct practical cryptosystems, in particular key encapsulation mechanism (KEM), resistant to quantum computing attacks. Nevertheless, there are still some inherent obstacles to NTRU-based KEM schemes in having integrated performance, taking security, bandwidth, error probability, and computational efficiency \emph{as a whole}, that is as good as and even better than their \{R,M\}LWE-based counterparts. In this work, we solve this problem by presenting a new family of NTRU-based KEM schemes, referred to as CTRU and CNTR. By bridging low-dimensional lattice codes and high-dimensional NTRU-lattice-based cryptography with careful design and analysis, to the best of our knowledge CTRU and CNTR are the first NTRU-based KEM schemes with scalable ciphertext compression via only one \emph{single} ciphertext polynomial, and are the first that could outperform \{R,M\}LWE-based KEM schemes in integrated performance. For instance, compared to Kyber that is currently the only standardized KEM by NIST, on the recommended parameter set CNTR-768 has about $12\%$ smaller ciphertext size while encapsulating 384-bit keys compared to the fixed 256-bit key size of Kyber, security strengthened by $(8,7)$ bits for classical and quantum security respectively, and significantly lower error probability ($2^{-230}$ of CNTR-768 vs. $2^{-164}$ of Kyber-768). In comparison with the state-of-the-art AVX2 implementation of Kyber-768, CNTR-768 is faster by 1.9X in KeyGen, 2.6X in Encaps, and 1.2X in Decaps, respectively. When compared to the NIST Round 3 finalist NTRU-HRSS, our CNTR-768 has about $15\%$ smaller ciphertext size, and the security is strengthened by $(55,49)$ bits for classical and quantum security respectively. As for the AVX2 implementation, CNTR-768 is faster than NTRU-HRSS by 19X in KeyGen, 2.3X in Encaps, and 1.6X in Decaps, respectively.

cs.CR↗

arXiv2020

Optimized Loss Functions for Object detection: A Case Study on Nighttime Vehicle Detection

Loss functions is a crucial factor that affecting the detection precision in object detection task. In this paper, we optimize both two loss functions for classification and localization simultaneously. Firstly, by multiplying an IoU-based coefficient by the standard cross entropy loss in classification loss function, the correlation between localization and classification is established. Compared to the existing studies, in which the correlation is only applied to improve the localization accuracy for positive samples, this paper utilizes the correlation to obtain the really hard negative samples and aims to decrease the misclassified rate for negative samples. Besides, a novel localization loss named MIoU is proposed by incorporating a Mahalanobis distance between predicted box and target box, which eliminate the gradients inconsistency problem in the DIoU loss, further improving the localization accuracy. Finally, sufficient experiments for nighttime vehicle detection have been done on two datasets. Our results show than when train with the proposed loss functions, the detection performance can be outstandingly improved. The source code and trained models are available at https://github.com/therebellll/NegIoU-PosIoU-Miou.

cs.CV↗