AES-RV: Hardware-Efficient RISC-V Accelerator with Low-Latency AES Instruction Extension for IoT Security

📅 2025-05-17
📈 Citations: 0
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🤖 AI Summary
To address the limitations of existing AES hardware accelerators in embedded and IoT applications—particularly concerning latency, energy efficiency, and configurability—this paper proposes a low-latency AES instruction-set extension accelerator tailored for the RISC-V architecture. The design introduces three key innovations: (1) a high-bandwidth on-die buffer architecture to minimize memory access latency; (2) a custom AES-specific instruction set enabling fine-grained control over cryptographic operations; and (3) a ping-pong DMA pipelining mechanism to sustain continuous data flow. The accelerator supports all standard AES modes (ECB, CBC, CTR, etc.) and key lengths (128-, 192-, and 256-bit) with real-time encryption/decryption capability. Implemented on a Xilinx ZCU102 FPGA, it achieves up to 255.97× higher throughput and 453.04× better energy efficiency compared to general-purpose CPU/GPU baselines. Moreover, its area-normalized throughput outperforms state-of-the-art AES accelerators.

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📝 Abstract
The Advanced Encryption Standard (AES) is a widely adopted cryptographic algorithm essential for securing embedded systems and IoT platforms. However, existing AES hardware accelerators often face limitations in performance, energy efficiency, and flexibility. This paper presents AES-RV, a hardware-efficient RISC-V accelerator featuring low-latency AES instruction extensions optimized for real-time processing across all AES modes and key sizes. AES-RV integrates three key innovations: high-bandwidth internal buffers for continuous data processing, a specialized AES unit with custom low-latency instructions, and a pipelined system supported by a ping-pong memory transfer mechanism. Implemented on the Xilinx ZCU102 SoC FPGA, AES-RV achieves up to 255.97 times speedup and up to 453.04 times higher energy efficiency compared to baseline and conventional CPU/GPU platforms. It also demonstrates superior throughput and area efficiency against state-of-the-art AES accelerators, making it a strong candidate for secure and high-performance embedded systems.
Problem

Research questions and friction points this paper is trying to address.

Enhancing AES performance for IoT security
Overcoming energy and flexibility limitations in AES accelerators
Optimizing real-time AES processing across all modes
Innovation

Methods, ideas, or system contributions that make the work stand out.

Low-latency AES instruction extensions for RISC-V
High-bandwidth internal buffers for continuous processing
Pipelined system with ping-pong memory transfer
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Van Tinh Nguyen
Van Tinh Nguyen
Unknown affiliation
Machine LearningRISC-VStochastic Computing
P
Phuc Hung Pham
Nara Institute of Science and Technology, 8916–5 Takayama-cho, Ikoma, Nara, 630-0192 Japan
V
Vu Trung Duong Le
Nara Institute of Science and Technology, 8916–5 Takayama-cho, Ikoma, Nara, 630-0192 Japan
H
Hoai Luan Pham
Nara Institute of Science and Technology, 8916–5 Takayama-cho, Ikoma, Nara, 630-0192 Japan
T
Tuan Hai Vu
Nara Institute of Science and Technology, 8916–5 Takayama-cho, Ikoma, Nara, 630-0192 Japan
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Thi Diem Tran
University of Information Technology, Vietnam National University, Ho Chi Minh City, 700000, Vietnam