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用于后量子密码的基于 RISC-V 的加速器.pdf

上传人: c** 编号:955326 2025-10-27 17页 1.85MB

1、A RISC-V BASED ACCELERATOR FOR POST QUANTUM CRYPTOGRAPHYAmbily Suresh,Manuel Freiberger,Andrew Wilson,Diego Gigena-Ivanovich,Willibald Krenn010203040506OVERVIEWRISC-V Research at SALThe ISOLDE ProjectRISC-V Accelerators for the EdgePQC The Classic McEliece AlgorithmHW and SW ImplementationSummary an

2、d Future workWhat do we do?SILICON AUSTRIA LABSSilicon Austria Labs GmbH3Silicon Austria Labs(SAL),established in 2018,is a European R&D center with a focus on the development of efficient and trustworthy technologies in the field of electronic systems.Industry-oriented researchR&D servicesWell-equi

3、pped research infrastructuresCustomized opportunities for co-operationTHE ISOLDE PROJECTSilicon Austria Labs GmbH4HIgh Performance,Safe,Secure,Open-Source Leveraged RISC-V Domain-Specific EcosystemsChips-JU+FFG funding for Austrian partnersMay 2023 to April 2026Around 40 partners from 9 countriesEnh

4、ance European high-performance RISC-V-based Systems-on-a-Chip(SoC)Development of advanced architectures,novel accelerators,and reusable IPsThis research received funding from the Austrian Research Promotion Agency and the Austrian ministry for Climate Action,Environment,Energy,Mobility,Innovation,an

5、d Technology under project FO999899263 and the Chips Joint Undertaking and its members Austria,Czechia,France,Germany,Italy,Romania,Spain,Sweden,and Switzerland under the ISOLDE project(no.101112274)RISC-V TOPICS SALSilicon Austria Labs GmbH5Post Quantum CryptographyEvaluate candidate algorithms for

6、 hardware implementationQuantum-safe encryption for resource constrained,edge environmentsEdge AIMinimize privacy risks through local AI acceleration.Speed up neural network processing for efficient,low-latency AI on edge devicesIntegration with RISC-V platformCustomizable and cost-effective RCHW-SW

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根据报告的内容,全文主要内容概括如下: - **项目背景**:Silicon Austria Labs (SAL) 在 RISC-V 研究领域,特别是后量子密码学(PQC)方面的工作。 - **ISOLDE 项目**:一个为期三年(2023-2026)的高性能、安全、开源的 RISC-V 基于系统芯片(SoC)项目,涉及40个合作伙伴。 - **RISC-V 研究方向**:包括 PQC、边缘 AI、硬件和软件协同开发等。 - **PQC 算法**:重点研究 McEliece 算法,并评估其硬件实现。 - **硬件实现**:优化 FFT 模块,用于密钥生成和错误定位多项式计算。 - **性能提升**:与现有算法相比,实现了速度提升。 - **未来计划**:开发用于分布式学习的 SoC,集成软件框架,优化算法和硬件设计。 核心数据: - McEliece 算法参数集:mceliece348864 (128-bit),mceliece460896 (192-bit),mceliece6688128 (256-bit) 等。 - 性能提升:加密/解密速度提升,内存使用优化。
量子加密新篇章?" RISC-V加速器如何应对未来加密挑战?" RISC-V加速器助力边缘AI发展!"
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