M<sup>2</sup>PI: Processing-in-Memory Modular Computing Accelerator for Full Homomorphic Encryption

LI Bing; LIU Huaijun; ZHANG Weigong

doi:10.11999/JEIT230349

Volume 45 Issue 9

Sep. 2023

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Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(9): 3302-3310

LI Bing, LIU Huaijun, ZHANG Weigong. M2PI: Processing-in-Memory Modular Computing Accelerator for Full Homomorphic Encryption[J]. Journal of Electronics & Information Technology, 2023, 45(9): 3302-3310. doi: 10.11999/JEIT230349

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LI Bing, LIU Huaijun, ZHANG Weigong. M²PI: Processing-in-Memory Modular Computing Accelerator for Full Homomorphic Encryption[J]. Journal of Electronics & Information Technology, 2023, 45(9): 3302-3310. doi: 10.11999/JEIT230349

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M²PI: Processing-in-Memory Modular Computing Accelerator for Full Homomorphic Encryption

doi: 10.11999/JEIT230349

1.
Academy for Multidisciplinary Studies, Capital Normal University, Beijing 100048, China
2.
College of Information Engineering, Capital Normal University, Beijing 100048, China

Funds: The National Natural Science Foundation of China (62204164)

Received Date: 2023-04-27
Rev Recd Date: 2023-08-18

Available Online: 2023-08-24

Publish Date: 2023-09-27

Abstract

Abstract

Fully Homomorphic Encryption (FHE) attracts emerging interests from the fields of medical diagnosis, cloud computing, machine learning, etc. because it can realize the calculation on encrypted data and improve significantly the security of private data in the cloud computing scenarios. However, the expensive computational cost of FHE prevents its wide application. Even after algorithm and software design optimization, the ciphertext data size of an integer plaintext in FHE reaches 56 MByte, and the secret key data size reaches 11 k Byte. The large size of ciphertext and key causes serious bottlenecks in computation and memory access. Processing-In-Memory (PIM) is an effective solution to this problem, which eliminates completely the efficiency and power problem of the memory wall, and enables the deployment of data-intensive of application to the edge side. The application of processing-in memory to accelerate fully homomorphic computing has been widely studied, but the execution of homomorphic encryption still faces the execution time bottleneck induced by time-consuming modular computing. The computational costs of various key operators in BFV encryption, decryption, and key generation operations are analyzed in this paper, and found that the average computational cost of modular computing reached 41%, with memory access accounting for 97%. A modular accelerator called Processing-In-Memory Modular(M²PI) based on Static Random-Access Memory(SRAM) array is proposed to optimize modular computing in full-homomorphic encryption. The experimental results show that the proposed work achieves 1.77 times speedup and 32.76 times energy saving compared to CPU.
- Fully Homomorphic Encryption(FHE),
- Processing-In-Memory(PIM),
- Modular computing

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References(21)

References

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