Memory-Efficient Designs for Word-Wise Universal Fully Homomorphic Encryption

2026-09-04

AI Summary

为解决全同态加密(FHE)内存瓶颈问题，提出BXT框架，通过压缩、序列化、延迟生成种子及剪枝等方法优化内存使用，提高计算效率。

Abstract

Fully Homomorphic Encryption (FHE) enables computation on encrypted data, preserving privacy throughout analysis. While its privacy is very strong, FHE is much slower to execute than the original computation. In particular, due to the recent success in accelerating its compute, the performance bottleneck shifts to the memory, especially considering that FHE magnifies the data size by orders of magnitude, resulting in a low arithmetic intensity. We propose BXT, an FHE optimization framework that mitigates the memory bottleneck through four techniques: (1) ciphertext compression, which regenerates ciphertext components from seeds during execution; (2) ciphertext serialization, which packs coefficients as bit arrays and unpacks them during L2-to-L1 transfer; (3) delayed seed generation, which defers PRNG-heavy offline work across aggregated operations; and (4) ciphertext digit pruning guided by fault-aware training tailored for Universal FHE. On CNN inference, the BXT-CSO50 configuration effectively achieves up to 3.8$\times$ speedup over the 100x GPU baseline with less than 1% accuracy loss at 50% comparison precision.

Problem

• Fully Homomorphic Encryption
• memory bottleneck
• arithmetic intensity

Innovation

• ciphertext compression
• ciphertext serialization
• delayed seed generation
• ciphertext digit pruning