VGG-16 Optimized Implementation

This page demonstrates the optimized VGG-16 inference workflow using FHEON. It highlights improvements in bootstrapping management, rotation key organization, and memory efficiency for encrypted CIFAR-10 inference.

Overview

The workflow consists of:

  1. Context and Key Generation - Initializes FHEController with parameters such as ring degree, slots, depth, and modulus chain. - Generates rotation and bootstrapping keys for convolution, average pooling, and fully connected layers. - Keys are serialized per layer, cleared after use, and reloaded during inference to optimize memory usage.

  2. Data Preparation - Loads CIFAR-10 test images from binary format. - Normalizes pixel values and encrypts them into ciphertext vectors.

  3. Optimized VGG-16 Layers - Implements convolution + ReLU blocks with optional bootstrapping. - Average pooling reduces spatial dimensions between stages. - Global average pooling before fully connected layers. - Fully connected layers finalize classification. - Bootstrapping is applied selectively to refresh ciphertext precision while minimizing overhead.

  4. Inference Loop - Iterates over encrypted CIFAR-10 images. - Loads layer-specific bootstrapping and rotation keys on-demand. - Sequentially applies convolution blocks, pooling, bootstrapping, and fully connected layers. - Decrypts outputs and writes predicted labels to file.

Key Functions

  • convolution_relu_block() – Convolution + bias addition + ReLU with optional bootstrapping.

  • FClayer_relu_block() – Fully connected layer with bias and optional ReLU, supporting level-aware encoding.

  • secure_optimzed_avgPool_multi_channels() – Homomorphic average pooling across multiple channels.

  • generate_bootstrapping_and_rotation_keys() – Layer-wise key generation for bootstrapping and rotations.

  • clear_context() – Clears memory of unused ciphertexts and keys to optimize resource usage.

Optimizations

  • Layer-wise Key Management: Rotation and bootstrapping keys are serialized and loaded per layer to reduce memory consumption.

  • Selective Bootstrapping: Applied only after critical convolution, poolings, and fully connected layers.

  • Memory Efficiency: Kernel and bias plaintexts are cleared after use; contexts are cleared after generating/loading keys.

  • Layer-wise Profiling: Execution time for convolution, pooling, and FC layers is measured and logged.

Full Example Source

You can view the full optimized VGG-16 implementation here:

VGG16Optimized.cpp