Area and Power Efficient FFT/IFFT Processor for FALCON Post-Quantum Cryptography
원문 초록
Quantum computing is an emerging technology on the verge of reshaping industries, while simultaneously challenging existing cryptographic algorithms. FALCON, a recent standard quantum-resistant digital signature, presents a challenging hardware implementation due to its extensive non-integer polynomial operations, necessitating FFT over the ring $\mathbb{Q}[x]/(x^n+1)$. This paper introduces an ultra-low power and compact processor tailored for FFT/IFFT operations over the ring, specifically optimized for FALCON applications on resource-constrained edge devices. The proposed processor incorporates various optimization techniques, including twiddle factor compression and conflict-free scheduling. In an ASIC implementation using a 22 nm GF process, the proposed processor demonstrates an area occupancy of 0.15 mm$^2$ and a power consumption of 12.6 mW at an operating frequency of 167 MHz. Since a hardware implementation of FFT/IFFT over the ring is currently non-existent, the execution time achieved by this processor is compared to the software implementation of FFT/IFFT of FALCON on a Raspberry Pi 4 with Cortex-A72, where the proposed processor achieves a speedup of up to 2.3$\times$. Furthermore, in comparison to dedicated state-of-the-art hardware accelerators for classic FFT, this processor occupies 42\% less area and consumes 83\% less power, on average. This suggests that the proposed hardware design offers a promising solution for implementing FALCON on resource-constrained devices.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Optimal Sensor Scheduling and Selection for Continuous-Discrete Kalman Filtering with Auxiliary Dynamics
SystolicAttention: Fusing FlashAttention within a Single Systolic Array
ANN-Based Grid Impedance Estimation for Adaptive Gain Scheduling in VSG Under Dynamic Grid Conditions
Confucius3-Math: A Lightweight High-Performance Reasoning LLM for Chinese K-12 Mathematics Learning
ConsumerBench: Benchmarking Generative AI Applications on End-User Devices
RapFlow-TTS: Rapid and High-Fidelity Text-to-Speech with Improved Consistency Flow Matching
토론
이 논문과 연결된 포럼 글이 아직 없습니다.