Crypto-RV: High-Efficiency FPGA-Based RISC-V Cryptographic Co-Processor for IoT Security

• URL: http://arxiv.org/abs/2602.04415v1
• Date: Wed, 04 Feb 2026 10:48:30 GMT
• Title: Crypto-RV: High-Efficiency FPGA-Based RISC-V Cryptographic Co-Processor for IoT Security
• Authors: Anh Kiet Pham, Van Truong Vo, Vu Trung Duong Le, Tuan Hai Vu, Hoai Luan Pham, Van Tinh Nguyen, Yasuhiko Nakashima,
• Abstract summary: cryptographic operations are critical for securing IoT, edge computing, and autonomous systems.<n>Current RISC-V platforms lack efficient hardware support for comprehensive cryptographic algorithm families and post-quantum cryptography.<n>This paper presents Crypto-RV, a RISC-V co-processor architecture that unifies support for SHA-256, SHA-512, SM3, SHA3-256, SHAKE-128, SHAKE-256 AES-128, HARAKA-256, and HARAKA-512 within a single 64-bit datapath.
• Score: 0.7622368504990539
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Cryptographic operations are critical for securing IoT, edge computing, and autonomous systems. However, current RISC-V platforms lack efficient hardware support for comprehensive cryptographic algorithm families and post-quantum cryptography. This paper presents Crypto-RV, a RISC-V co-processor architecture that unifies support for SHA-256, SHA-512, SM3, SHA3-256, SHAKE-128, SHAKE-256 AES-128, HARAKA-256, and HARAKA-512 within a single 64-bit datapath. Crypto-RV introduces three key architectural innovations: a high-bandwidth internal buffer (128x64-bit), cryptography-specialized execution units with four-stage pipelined datapaths, and a double-buffering mechanism with adaptive scheduling optimized for large-hash. Implemented on Xilinx ZCU102 FPGA at 160 MHz with 0.851 W dynamic power, Crypto-RV achieves 165 times to 1,061 times speedup over baseline RISC-V cores, 5.8 times to 17.4 times better energy efficiency compared to powerful CPUs. The design occupies only 34,704 LUTs, 37,329 FFs, and 22 BRAMs demonstrating viability for high-performance, energy-efficient cryptographic processing in resource-constrained IoT environments.

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