Back to Search
Start Over
PASNet: Polynomial Architecture Search Framework for Two-party Computation-based Secure Neural Network Deployment
- Source :
- DAC 2023
- Publication Year :
- 2023
-
Abstract
- Two-party computation (2PC) is promising to enable privacy-preserving deep learning (DL). However, the 2PC-based privacy-preserving DL implementation comes with high comparison protocol overhead from the non-linear operators. This work presents PASNet, a novel systematic framework that enables low latency, high energy efficiency & accuracy, and security-guaranteed 2PC-DL by integrating the hardware latency of the cryptographic building block into the neural architecture search loss function. We develop a cryptographic hardware scheduler and the corresponding performance model for Field Programmable Gate Arrays (FPGA) as a case study. The experimental results demonstrate that our light-weighted model PASNet-A and heavily-weighted model PASNet-B achieve 63 ms and 228 ms latency on private inference on ImageNet, which are 147 and 40 times faster than the SOTA CryptGPU system, and achieve 70.54% & 78.79% accuracy and more than 1000 times higher energy efficiency.<br />Comment: DAC 2023 accepeted publication, short version was published on AAAI 2023 workshop on DL-Hardware Co-Design for AI Acceleration: RRNet: Towards ReLU-Reduced Neural Network for Two-party Computation Based Private Inference
- Subjects :
- Computer Science - Cryptography and Security
E.3
I.2
B.0
Subjects
Details
- Database :
- arXiv
- Journal :
- DAC 2023
- Publication Type :
- Report
- Accession number :
- edsarx.2306.15513
- Document Type :
- Working Paper