Welcome to the resource topic for 2023/532
Title:
HLG: A framework for computing graphs in Residue Number System and its application in Fully Homomorphic Encryption
Authors: Shuang Wu, Chunhuan Zhao, Ye Yuan, Shuzhou Sun, Jie Li, Yamin Liu
Abstract:Implementation of Fully Homomorphic Encryption (FHE) is challenging. Especially when considering hardware acceleration, the major performance bottleneck is data transfer. Here we propose an algebraic framework called Heterogenous Lattice Graph (HLG) to build and process computing graphs in Residue Number System (RNS), which is the basis of high performance implementation of mainstream FHE algorithms.
There are three main design goals for HLG framework:
• Design a dedicated IR (HLG IR) for RNS system, here splitting and combination of data placeholders has practical implications in an algebraic sense. Existing IRs cannot efficiently support these operations.
• Lower the technical barriers for both crypto researchers and hardware engineers by decoupling front-end cryptographic algorithms from the back-end hardware platforms. The algorithms and solutions built on HLG framework can be written once and run everywhere. Researchers and engineers don’t need to understand each other.
• Try to reduce the cost of data transfer between CPU and GPU/FPGA/dedicated hardware, by providing the intermediate representation (IR) of the computing graph for hardware compute engine, which allows task scheduling without help from CPU.
We have implemented CKKS algorithm based on HLG framework, together with a compute engine for multiple CPU cores. Experiment shows that we can outperform SEAL v3 Library in several use cases in multi-threading scenarios.
ePrint: https://eprint.iacr.org/2023/532
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