Title: SpENCNN: Orchestrating Encoding and Sparsity for Fast Homomorphically Encrypted Neural Network Inference. Abstract: Homomorphic Encryption (HE) is a promising technology for protecting user's data privacy for Machine Learning as a Service (MLaaS) on public clouds. However, the computation overheads associated with the HE operations, which can be orders of magnitude slower than their counterparts for plaintexts, can lead to extremely high latency in neural network inference, seriously hindering its application in practice. While extensive neural network optimization techniques have been proposed, such as sparsification and pruning for plaintext domain, they cannot address this problem effectively. In this paper, we propose an HE-based CNN inference framework, i.e., SpENCNN, that can effectively exploit the single-instruction-multiple-data (SIMD) feature of the HE scheme to improve the CNN inference latency. In particular, we first develop a HE-group convolution technique that can partition channels among different groups based on the data size and ciphertext size, and then encode them into the same ciphertext in an interleaved manner, so as to dramatically reduce the bottlenecked operations in HE convolution. We further develop a sub-block weight pruning technique that can reduce more costly HE-operations for CNN convolutions. Our experiment results show that the SpENCNN-optimized CNN models can achieve overall speedups of 8.37x, 12.11x, and 19.26x for LeNet, VGG-5, and HEFNet, respectively, with negligible accuracy loss.