2021
DOI: 10.1109/tc.2021.3078691
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High-Performance FPGA Accelerator for SIKE

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Cited by 12 publications
(3 citation statements)
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“…SIKE's performance on embedded devices may be an issue because the time to perform a single key encapsulation/decapsulation (on a low-end 32-bit ARM processor, for instance) can be noticeable. Implementing SIKE in FPGAs may be a good route to achieving better performance in embedded devices [224,225]. In addition, it may be attractive to construct hybrid protocols that use SIKE together with pre-quantum-secure ECDH (elliptic curve Diffie-Hellman key exchange), since SIKE and ECDH can share some common subroutines.…”
Section: Sike (Supersingular Isogeny Key Encapsulationmentioning
confidence: 99%
“…SIKE's performance on embedded devices may be an issue because the time to perform a single key encapsulation/decapsulation (on a low-end 32-bit ARM processor, for instance) can be noticeable. Implementing SIKE in FPGAs may be a good route to achieving better performance in embedded devices [224,225]. In addition, it may be attractive to construct hybrid protocols that use SIKE together with pre-quantum-secure ECDH (elliptic curve Diffie-Hellman key exchange), since SIKE and ECDH can share some common subroutines.…”
Section: Sike (Supersingular Isogeny Key Encapsulationmentioning
confidence: 99%
“…SIKE's performance on embedded devices may be an issue because the time to perform a single key encapsulation/decapsulation (on a low-end 32-bit ARM processor, for instance) can be noticeable. Implementing SIKE in FPGAs may be a good route to achieving better performance in embedded devices [222,223]. In addition, it may be attractive to construct hybrid protocols that use SIKE together with pre-quantum-secure ECDH (elliptic curve Diffie-Hellman key exchange), since SIKE and ECDH can share some common subroutines.…”
Section: Sike (Supersingular Isogeny Key Encapsulationmentioning
confidence: 99%
“…As shown in Table I, a variety of FPGA products ranging from embedded systems, i.e., Xilinx Zynq-7000 with only 3,600 logic elements (≈$130), to data center devices, i.e., Agilex F R25A with 2.6 millions of logic elements (≈$10k), are available in the market. Various FPGA-based applicationspecific accelerators, such as for deep convolutional neural networks [76], [96], recurrent neural networks [53], classic and post-quantum cryptographic algorithms [28], [33], Memcached [51], etc., have been proposed and deployed on such devices [44], [65].…”
Section: Introductionmentioning
confidence: 99%