Fully homomorphic encryption deep learning reasoning method and system based on FPGA
A fully homomorphic encryption and deep learning technology, applied in the field of homomorphic encryption algorithms, it can solve the problems of complex computing, complexity, the combination of homomorphic encryption and deep learning, etc., and achieve the effect of speeding up the inference speed and improving the inference speed.
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Embodiment 1
[0051] Such as figure 1 As shown, this embodiment provides an FPGA-based fully homomorphic encryption deep learning reasoning method, including:
[0052] S1: Obtain the ciphertext encrypted by the homomorphic encryption algorithm and the corresponding encoded plaintext;
[0053] S2: Obtain the multiplication depth, data processing scale and network layer of the initial deep learning network;
[0054] S3: Determine the value range of the coefficient modulus of the polynomial according to the number of items of the plaintext polynomial, determine the value number according to the multiplication depth, and determine the coefficient modulus according to the coefficient modulus selected by the error parameter;
[0055] S4: Determine the weight and deviation of the network layer according to the number of items and coefficient modulus of the polynomial and the scale of data processing, so as to obtain the packaging strategy of the network layer;
[0056] S5: According to the packa...
Embodiment 2
[0100] This embodiment provides an FPGA-based fully homomorphic encryption deep learning reasoning system, including:
[0101] The first obtaining module is used to obtain the ciphertext encrypted by the homomorphic encryption algorithm and the corresponding encoded plaintext;
[0102] The second acquisition module is used to acquire the multiplication depth, data processing scale and network layer of the initial deep learning network;
[0103] The homomorphic encryption parameter determination module is used to determine the value range of the coefficient modulus of the polynomial according to the number of items of the plaintext polynomial, determine the value number according to the multiplication depth, and determine the coefficient modulus according to the coefficient modulus selected by the error parameter;
[0104] A packaging strategy determination module is used to determine the weight and deviation of the network layer according to the number of items and coefficient...
Embodiment 3
[0108] According to the network structure and homomorphic encryption parameters, the optimal design of the network model software level is obtained. However, the complex memory access mode hinders the direct use of multi-threading and other parallel technologies for optimization. Therefore, directly deploying it on the CPU cannot fully utilize the system. Performance; in order to achieve optimal performance indicators such as delay, throughput, and power consumption of the entire system, this embodiment provides a fully homomorphic encryption deep learning reasoning platform based on FPGA, such as Figure 5 shown;
[0109] The platform adopts the code system and HLS tools to construct a reasoning model based on the FPGA-based fully homomorphic encryption deep learning reasoning method described in Example 1, and realizes the homomorphic encryption data reasoning method. The deep learning network that supports CKKS homomorphic encryption will use high The hierarchical synthesis...
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