利用稀疏性加速加密神经网络计算，提升隐私保护下的推理效率。
Exploiting Unstructured Sparsity in Fully Homomorphic Encrypted DNNs
- 在全同态加密中设计三种稀疏矩阵乘法方案，适配任意稀疏结构。
- 50%稀疏度下平均提速2.5倍，64核多线程达32.5倍加速。
- 显著降低加密矩阵存储开销，适合高稀疏场景的隐私计算应用。
深度神经网络在隐私敏感环境中的部署受限于全同态加密（FHE）带来的计算开销。本文探索在FHE矩阵乘法中利用无结构稀疏性以减轻负担，同时满足模型精度要求。我们证明稀疏性可在任意矩阵乘法中被有效利用，在所有稀疏度下均优于基线朴素算法，这与明文域中稀疏性与稀疏乘法开销之间的权衡形成鲜明对比。为此，我们基于常见的明文稀疏编码，提出了三种FHE稀疏乘法方案。性能提升与具体方案无关；但部分方案在高稀疏度下显著降低加密矩阵存储需求。所提方案在50%无结构稀疏度下实现平均2.5倍性能提升，使用64个核心的多线程方案相比单线程稀疏计算提速32.5倍。
原文摘要 · Abstract (English)
The deployment of deep neural networks (DNNs) in privacy-sensitive environments is constrained by computational overheads in fully homomorphic encryption (FHE). This paper explores unstructured sparsity in FHE matrix multiplication schemes as a means of reducing this burden while maintaining model accuracy requirements. We demonstrate that sparsity can be exploited in arbitrary matrix multiplication, providing runtime benefits compared to a baseline naive algorithm at all sparsity levels. This is a notable departure from the plaintext domain, where there is a trade-off between sparsity and the overhead of the sparse multiplication algorithm. In addition, we propose three sparse multiplication schemes in FHE based on common plaintext sparse encodings. We demonstrate the performance gain is scheme-invariant; however, some sparse schemes vastly reduce the memory storage requirements of the encrypted matrix at high sparsity values. Our proposed sparse schemes yield an average performance gain of 2.5x at 50% unstructured sparsity, with our multi-threading scheme providing a 32.5x performance increase over the equivalent single-threaded sparse computation when utilizing 64 cores.
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