PASNet: Polynomial Architecture Search Framework for Two-party Computation-based Secure Neural Network Deployment

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.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 Inferenc

PolyMPCNet: Towards ReLU-free Neural Architecture Search in Two-party Computation Based Private Inference

The rapid growth and deployment of deep learning (DL) has witnessed emerging privacy and security concerns. To mitigate these issues, secure multi-party computation (MPC) has been discussed, to enable the privacy-preserving DL computation. In practice, they often come at very high computation and communication overhead, and potentially prohibit their popularity in large scale systems. Two orthogonal research trends have attracted enormous interests in addressing the energy efficiency in secure deep learning, i.e., overhead reduction of MPC comparison protocol, and hardware acceleration. However, they either achieve a low reduction ratio and suffer from high latency due to limited computation and communication saving, or are power-hungry as existing works mainly focus on general computing platforms such as CPUs and GPUs. In this work, as the first attempt, we develop a systematic framework, PolyMPCNet, of joint overhead reduction of MPC comparison protocol and hardware acceleration, by integrating hardware latency of the cryptographic building block into the DNN loss function to achieve high energy efficiency, accuracy, and security guarantee. Instead of heuristically checking the model sensitivity after a DNN is well-trained (through deleting or dropping some non-polynomial operators), our key design principle is to em enforce exactly what is assumed in the DNN design -- training a DNN that is both hardware efficient and secure, while escaping the local minima and saddle points and maintaining high accuracy. More specifically, we propose a straight through polynomial activation initialization method for cryptographic hardware friendly trainable polynomial activation function to replace the expensive 2P-ReLU operator. We develop a cryptographic hardware scheduler and the corresponding performance model for Field Programmable Gate Arrays (FPGA) platform

AutoReP: Automatic ReLU Replacement for Fast Private Network Inference

The growth of the Machine-Learning-As-A-Service (MLaaS) market has highlighted clients' data privacy and security issues. Private inference (PI) techniques using cryptographic primitives offer a solution but often have high computation and communication costs, particularly with non-linear operators like ReLU. Many attempts to reduce ReLU operations exist, but they may need heuristic threshold selection or cause substantial accuracy loss. This work introduces AutoReP, a gradient-based approach to lessen non-linear operators and alleviate these issues. It automates the selection of ReLU and polynomial functions to speed up PI applications and introduces distribution-aware polynomial approximation (DaPa) to maintain model expressivity while accurately approximating ReLUs. Our experimental results demonstrate significant accuracy improvements of 6.12% (94.31%, 12.9K ReLU budget, CIFAR-10), 8.39% (74.92%, 12.9K ReLU budget, CIFAR-100), and 9.45% (63.69%, 55K ReLU budget, Tiny-ImageNet) over current state-of-the-art methods, e.g., SNL. Morever, AutoReP is applied to EfficientNet-B2 on ImageNet dataset, and achieved 75.55% accuracy with 176.1 times ReLU budget reduction.Comment: ICCV 2023 accepeted publicatio

Neural Network Energy Management Strategy for Series Hybrid Electric Car

A design concept of energy management and control strategy for hybrid electric car based on neural network and global optimization is proposed. The control strategy can effectively combine the advantages of global optimization algorithm and neural network algorithm. The minimum fuel consumption of the engine model can be derived. Simulation and analysis of the known road cycle conditions were carried out. The simulation platform ADVISOR2002 is used for the secondary development. The control strategy, the power monitoring control strategy and the thermostat control strategy were simulated and compared. The strategy has a strong adaptive capacity which can further improve the fuel economy of hybrid car

Atomically Dispersed ZnN4 Sites Anchored on P‐Functionalized Carbon with Hierarchically Ordered Porous Structures for Boosted Electroreduction of CO2

Abstract Tuning the coordination structures of metal sites is intensively studied to improve the performances of single‐atom site catalysts (SASC). However, the pore structure of SASC, which is highly related to the accessibility of active sites, has received little attention. In this work, single‐atom ZnN4 sites embedded in P‐functionalized carbon with hollow‐wall and 3D ordered macroporous structure (denoted as H‐3DOM‐ZnN4/P‐C) are constructed. The creation of hollow walls in ordered macroporous structures can largely increase the external surface area to expose more active sites. The introduction of adjacent P atoms can optimize the electronic structure of ZnN4 sites through long‐rang regulation to enhance the intrinsic activity and selectivity. In the electrochemical CO2 reduction reaction, H‐3DOM‐ZnN4/P‐C exhibits high CO Faradaic efficiency over 90% in a wide potential window (500 mV) and a large turnover frequency up to 7.8 × 104 h−1 at −1.0 V versus reversible hydrogen electrode, much higher than its counterparts without the hierarchically ordered structure or P‐functionalization

Long noncoding RNA NORAD, a novel competing endogenous RNA, enhances the hypoxia-induced epithelial-mesenchymal transition to promote metastasis in pancreatic cancer

Abstract Background Pancreatic cancer, one of the top two most fatal cancers, is characterized by a desmoplastic reaction that creates a dense microenvironment, promoting hypoxia and inducing the epithelial-to-mesenchymal transition (EMT) to facilitate invasion and metastasis. Recent evidence indicates that the long noncoding RNA NORAD may be a potential oncogenic gene and that this lncRNA is significantly upregulated during hypoxia. However, the overall biological role and clinical significance of NORAD remains largely unknown. Methods NORAD expression was measured in 33 paired cancerous and noncancerous tissue samples by real-time PCR. The effects of NORAD on pancreatic cancer cells were studied by overexpression and knockdown in vitro. Insights into the mechanism of competitive endogenous RNAs (ceRNAs) were gained from bioinformatics analyses and luciferase assays. In vivo, metastatic potential was identified using an orthotopic model of PDAC and quantified using bioluminescent signals. Alterations in RhoA expression and EMT levels were identified and verified by immunohistochemistry and Western blotting. Results NORAD is highly expressed in pancreatic cancer tissues and upregulated in hypoxic conditions. NORAD upregulation is correlated with shorter overall survival in pancreatic cancer patients. Furthermore, NORAD overexpression promoted the migration and invasion of pancreatic carcinoma cells, while NORAD depletion inhibited EMT and metastasis in vitro and in vivo. In particular, NORAD may function as a ceRNA to regulate the expression of the small GTP binding protein RhoA through competition for hsa-miR-125a-3p, thereby promoting EMT. Conclusions Elevated expression of NORAD in pancreatic cancer tissues is linked to poor prognosis and may confer a malignant phenotype upon tumor cells. NORAD may function as a ceRNA to regulate the expression of the small GTP binding protein RhoA through competition for hsa-miR-125a-3p. This finding may contribute to a better understanding of the role played by lncRNAs in hypoxia-induced EMT and provide a potential novel diagnostic and therapeutic target for pancreatic cancer