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As the application of deep learning continues to grow, so does the amount ofdata used to make predictions. While traditionally, big-data deep learning wasconstrained by computing performance and off-chip memory bandwidth, a newconstraint has emerged: privacy. One solution is homomorphic encryption (HE).Applying HE to the client-cloud model allows cloud services to performinference directly on the client's encrypted data. While HE can meet privacyconstraints, it introduces enormous computational challenges and remainsimpractically slow in current systems.  This paper introduces Cheetah, a set of algorithmic and hardwareoptimizations for HE DNN inference to achieve plaintext DNN inference speeds.Cheetah proposes HE-parameter tuning optimization and operator schedulingoptimizations, which together deliver 79x speedup over the state-of-the-art.However, this still falls short of plaintext inference speeds by almost fourorders of magnitude. To bridge the remaining performance gap, Cheetah furtherproposes an accelerator architecture that, when combined with the algorithmicoptimizations, approaches plaintext DNN inference speeds. We evaluate severalcommon neural network models (e.g., ResNet50, VGG16, and AlexNet) and show thatplaintext-level HE inference for each is feasible with a custom acceleratorconsuming 30W and 545mm^2.

Cheetah: Optimizing and Accelerating Homomorphic Encryption for Private Inference