Skip-Connection Replacement for Homomorphic Encryption Latency
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Modern deep learning applications with skip-connections face high costs during model inference under Homomorphic Encryption (HE), particularly in third-party cloud environments, and existing solutions either remove skip connections or accept their placement without optimizing performance.
Innovation Solution
The method involves training HE-friendly deep convolutional neural networks (CNNs) without skip-connections and replacing mid-term skip-connections with short-term Dirac parameterization and long-term shared-source skip-connections to reduce latency and maintain accuracy, thereby improving computing power for encrypted operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If skip-connections are used in deep learning networks to improve accuracy, then model performance is improved, but latency and computational cost under Homomorphic Encryption increase significantly
Solution Approach 1:
The patent removes skip-connections from the neural network architecture when deploying under Homomorphic Encryption, extracting the problematic component that causes high latency and computational cost while preserving the core convolutional layers that provide accuracy
Solution Approach 2:
The patent modifies the network architecture by changing the parameter of skip-connection presence from true to false, and replaces them with alternative structures like identity mappings or direct connections that maintain information flow without the encryption overhead of skip-connections
2Loss of time
If skip-connections are removed to reduce latency under HE, then inference speed is improved, but model accuracy may degrade
Solution Approach 1:
The patent introduces intermediary structures such as identity mappings or direct connections that serve as mediators to preserve information flow between layers, replacing the functional role of skip-connections without incurring their encryption overhead
Solution Approach 2:
The patent segments the network into blocks where skip-connections are removed, and replaces them with localized alternative structures that maintain gradient flow and information propagation within each segment
3Reliability
If Homomorphic Encryption is applied to protect data privacy in cloud environments, then data confidentiality is improved, but computational cost and latency increase
Solution Approach 1:
The patent extracts and removes the skip-connection operations from the encrypted computation path, eliminating the specific source of high latency while maintaining the Homomorphic Encryption framework for data protection
Solution Approach 2:
The patent changes the computational parameters by removing operations that require expensive homomorphic operations (skip-connections) and replacing them with structures that minimize encrypted computation overhead
4Measurement precision
If modern deep learning architectures with many skip-connections are used, then accuracy on image processing tasks is improved, but the network becomes costly to execute under HE
Solution Approach 1:
The patent extracts and removes the complex skip-connection structures from the deep learning architecture, simplifying the network to contain only the essential convolutional layers needed for image processing accuracy
Solution Approach 2:
The patent inverts the conventional approach by starting with a skip-connection-free architecture and selectively adding only those connections necessary for HE compatibility, rather than removing connections from a skip-connection-heavy architecture
Data Source
AI summary
A skip-connections analysis method, system, and computer program product for accelerating neural networks by removing skip-connections and efficient skip-connection realization.