Accelerating Homomorphic Evaluation on Reconfigurable Hardware

By Thomas Pöppelmann, Michael Naehrig, Andrew Putnam, Adrián Macías. 2015

70 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

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Citations in the corpus

HEAX: An Architecture for Computing on Encrypted Data (2019, background): “Pöppelmann et al. [51] have also proposed an architecture for YASHE scheme.”
A Survey on Homomorphic Encryption Schemes (2017, background): “Such large and reconfigurable environment provided by FPGAs motivates many researchers to speed up the practicality of FHE schemes [Cousins et al. 2012; Wang and Huang 2013; Cao et al. 2013; Moore et al. 2013; Chen et al. 2015; Cao et al. 2014; Moore et al. 2014a; Cousins et al. 2014; Roy et al. 201”
HEPCloud: An FPGA-Based Multicore Processor for FV Somewhat Homomorphic Function Evaluation (2018, methodology): “Hence, it is not surprising that during the recent years several publications [8], [13], [14], [15], [26], [28], [31], [33], [40], [41] have reported results”
Cheetah: Optimizing and Accelerating Homomorphic Encryption for Private Inference (2020, methodology): “NTT has been ported to FPGAs [46], [50] and GPUs [2], [3], [17] to speedup polynomial multiplication.”
FPGA-Based High-Performance Parallel Architecture for Homomorphic Computing on Encrypted Data (2019, methodology): “[14] implemented the YASHE [8] scheme in the Catapult [34] architecture which is an FPGA-based domain specific accelerator for cloud computing applications.”
HEAX: High-Performance Architecture for Computation on Homomorphically Encrypted Data in the Cloud (2019, background): “However, the performance degradation due to the offchip memory accesses are not considered in this work which can be the main bottleneck as reported by prior art [67, 60].”