MetaKernel: Enabling Efficient Encrypted Neural Network Inference through Unified MVM and Convolution
Peng Yuan, Yan Liu, JianXin Lai, Long Li, Tianxiang Sui, Linjie Xiao, Xiaojing Zhang, Qing Yang Zhu, Jingling Xue · Proceedings of the ACM on Programming Languages · 2025
Practical encrypted neural network inference under the CKKS fully homomorphic encryption (FHE) scheme relies heavily on accelerating two key kernel operations: Matrix-Vector Multiplication (MVM) and Convolution (Conv). However, existing solutions—such as expert-tuned libraries and domain-specific languages—are designed in an ad hoc manner, leading to significant inefficiencies caused by excessive rotations. We introduce MKR, a novel composition-based compiler approach that optimizes MVM and Conv kernel operations for DNN models under CKKS within a unified framework. MKR decomposes each kernel into composable units, called MetaKernels , to enhance SIMD parallelism within ciphertexts (via horizontal batching) and computational parallelism across them (via vertical batching). Our approach tackles previously unaddressed challenges, including reducing rotation overhead through a rotation-aware cost model for data packing, while also ensuring high slot utilization, uniform handling of inputs with arbitrary sizes, and compatibility with the output tensor layout. Implemented in a production-quality FHE compiler, MKR achieves inference time speedups of 10.08×−185.60× for individual MVM and Conv kernels and 1.75×−11.84× for end-to-end inference compared to a state-of-the-art FHE compiler. Moreover, MKR enables homomorphic execution of large DNN models, where prior methods fail, significantly advancing the practicality of FHE compilers.