SAILOR：一种可扩展且节能的超轻量RISC-V用于物联网安全
SAILOR: A Scalable and Energy-Efficient Ultra-Lightweight RISC-V for IoT Security
AI总结：
SAILOR是一种面向物联网安全的超轻量RISC-V核心，通过模块化设计和可调节数据路径，在保证性能的同时实现低功耗和低硬件成本。
摘要：
近年来，RISC-V已促进物联网设备的发展，需要在能源效率、紧凑面积和集成安全之间取得平衡的架构。然而，大多数最近的物联网RISC-V核心优先考虑面积足迹或能源效率，而添加加密支持进一步影响紧凑性。因此，真正集成的架构在同时优化效率和安全方面仍然鲜有探索，使受限的物联网环境面临性能和安全权衡的威胁。在本文中，我们介绍了SAILOR，一种用于物联网加密应用的节能且可扩展的超轻量RISC-V核心家族。我们的设计是模块化的，并覆盖1位、2位、4位、8位、16位和32位的串行执行数据路径，优先考虑最小面积。这种模块化设计和可调节的数据路径减少了集成RISC-V加密扩展的开销，实现了低硬件成本的同时显著提高了能源效率。我们通过全面分析面积、能源和效率的权衡来验证我们的设计方法。结果在性能和能源效率方面均优于现有最佳解决方案，最高提升达13倍，面积减少最高达59%，证明了轻量级加密功能可以在不带来重大开销的情况下添加，并且在能源或面积高效的设计中不需要牺牲性能。
英文摘要：
Recently, RISC-V has contributed to the development of IoT devices, requiring architectures that balance energy efficiency, compact area, and integrated security. However, most recent RISC-V cores for IoT prioritize either area footprint or energy efficiency, while adding cryptographic support further compromises compactness. As a result, truly integrated architectures that simultaneously optimize efficiency and security remain largely unexplored, leaving constrained IoT environments vulnerable to performance and security trade-offs. In this paper, we introduce SAILOR, an energy-efficient and scalable ultra-lightweight RISC-V core family for cryptographic applications in IoT. Our design is modular and spans 1-, 2-, 4-, 8-, 16-, and 32-bit serialized execution data-paths, prioritizing minimal area. This modular design and adaptable data-path minimizes the overhead of integrating RISC-V cryptography extensions, achieving low hardware cost while significantly improving energy efficiency. We validate our design approach through a comprehensive analysis of area, energy, and efficiency trade-offs. The results surpass state-of-the-art solutions in both performance and energy efficiency by up to 13x and reduce area by up to 59 %, demonstrating that lightweight cryptographic features can be added without prohibitive overhead, and that energy- or area-efficient designs need not compromise performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
