An Enhanced Data Packing Method for General Matrix Multiplication in Brakerski/Fan-Vercauteren Scheme
General Matrix-Matrix Multiplication (GEMM) stands as the most ubiquitous operation in machine learning applications. However, performing GEMM within Fully Homomorphic Encryption (FHE) is inefficient due to high computational demands and significant data migration constrained by limited bandwidth. A...
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| Vydané v: | 2025 62nd ACM/IEEE Design Automation Conference (DAC) s. 1 - 7 |
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22.06.2025
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| Abstract | General Matrix-Matrix Multiplication (GEMM) stands as the most ubiquitous operation in machine learning applications. However, performing GEMM within Fully Homomorphic Encryption (FHE) is inefficient due to high computational demands and significant data migration constrained by limited bandwidth. Additionally, the inherent limitations of FHE schemes restrict the widespread application of machine learning, as standard activation functions are incompatible. This incompatibility necessitates alternative nonlinear functions, which lead to notable accuracy reductions. To address these challenges, we introduce a polynomial encoding methodology for GEMM under the Brakerski/Fan-Vercauteren (BFV) scheme and extend the method to inference with packing inputs and weights for different sizes. Furthermore, we design specialized hardware to accelerate the inference process through optimized scheduling between the hardware and the host system. In experiments, we implemented our hardware on an FPGA U250 platform. Compared to existing solutions, our method achieves superior performance, achieving the highest 4.22 \times and 3.99 \times speedups on MNIST and CIFAR-10. |
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| AbstractList | General Matrix-Matrix Multiplication (GEMM) stands as the most ubiquitous operation in machine learning applications. However, performing GEMM within Fully Homomorphic Encryption (FHE) is inefficient due to high computational demands and significant data migration constrained by limited bandwidth. Additionally, the inherent limitations of FHE schemes restrict the widespread application of machine learning, as standard activation functions are incompatible. This incompatibility necessitates alternative nonlinear functions, which lead to notable accuracy reductions. To address these challenges, we introduce a polynomial encoding methodology for GEMM under the Brakerski/Fan-Vercauteren (BFV) scheme and extend the method to inference with packing inputs and weights for different sizes. Furthermore, we design specialized hardware to accelerate the inference process through optimized scheduling between the hardware and the host system. In experiments, we implemented our hardware on an FPGA U250 platform. Compared to existing solutions, our method achieves superior performance, achieving the highest 4.22 \times and 3.99 \times speedups on MNIST and CIFAR-10. |
| Author | Tan, Yan Jiang, Zijun Lyu, Yangdi Meng, Xiangchen |
| Author_xml | – sequence: 1 givenname: Xiangchen surname: Meng fullname: Meng, Xiangchen email: xmeng027@connect.hkust-gz.edu.cn organization: The Hong Kong University of Science and Technology (Guangzhou) Guangzhou,Microelectronics Thrust,Guangdong,China – sequence: 2 givenname: Yan surname: Tan fullname: Tan, Yan email: ytan910@connect.hkust-gz.edu.cn organization: The Hong Kong University of Science and Technology (Guangzhou) Guangzhou,Microelectronics Thrust,Guangdong,China – sequence: 3 givenname: Zijun surname: Jiang fullname: Jiang, Zijun email: zjiang438@connect.hkust-gz.edu.cn organization: The Hong Kong University of Science and Technology (Guangzhou) Guangzhou,Microelectronics Thrust,Guangdong,China – sequence: 4 givenname: Yangdi surname: Lyu fullname: Lyu, Yangdi email: yangdilyu@hkust-gz.edu.cn organization: The Hong Kong University of Science and Technology (Guangzhou) Guangzhou,Microelectronics Thrust,Guangdong,China |
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| Snippet | General Matrix-Matrix Multiplication (GEMM) stands as the most ubiquitous operation in machine learning applications. However, performing GEMM within Fully... |
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| SubjectTerms | Accuracy Encoding Field programmable gate arrays FPGA Fully Homomorphic Encryption Hardware Homomorphic encryption Machine learning Matrix Multiplication Neural networks Polynomials Schedules |
| Title | An Enhanced Data Packing Method for General Matrix Multiplication in Brakerski/Fan-Vercauteren Scheme |
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