FPGA Accelerator for Homomorphic Encrypted Sparse Convolutional Neural Network Inference

Abstract

Homomorphic Encryption (HE) is a promising solution to the increasing concerns of privacy in machine learning. But HE-based CNN inference remains impractically slow. Pruning can significantly reduce the compute and memory footprint of CNNs. However, homomorphic encrypted Sparse Convolutional Neural Networks (SCNN) have vastly different compute and memory characteristics compared with unencrypted SCNN. Simply extending the design principles of existing SCNN accelerators may offset the potential acceleration offered by sparsity. To realize fast execution, we propose an FPGA accelerator to speedup the computation of linear layers, the main computational bottleneck in HE SCNN batch inference. First, we analyze the memory requirements of various linear layers in HE SCNN and discuss the unique challenges. Motivated by the analysis, we present a novel dataflow specially designed to optimize HE SCNN data reuse coupled with an efficient scheduling policy that minimizes on-chip SRAM access conflicts. Leveraging the proposed dataflow and scheduling algorithm, we demonstrate the first end-to-end acceleration of HE SCNN batch inference targeting CPU-FPGA heterogeneous platforms. For a batch of 8K images, our design achieves up to 5.6× speedup in inference latency compared with the CPU-only solution for widely studied 6-layer and 11-layer HE CNNs.