Operator fusion is essential for accelerating FHE-based DNN inference because it reduces multiplicative depth and, in turn, lowers the cost of ciphertext operations by keeping them at lower ciphertext levels. Existing approaches either rely on manual optimizations, which miss cross-operator opportunities, or on compiler pattern matching, which lacks generality. Standard DNN graphs omit FHE-specific behaviors, while fully lowering to primitive FHE operations introduces excessive granularity and obstructs effective optimization.We present FHEFusion, a compiler framework for the CKKS scheme that enables fusion through a new IR. This IR preserves high-level DNN semantics while introducing FHE-aware operators—masking and compaction (Strided_Slice)—that are central to CKKS, thereby exposing broader fusion opportunities. Guided by algebraic rules and an FHE-aware cost model, FHEFusion reduces multiplicative depth and identifies profitable fusions. Integrated into ANT-ACE, a state-of-the-art FHE compiler, FHEFusion outperforms NGRAPH, the only framework with graph-level fusion, achieving up to 3.02× (average 1.40×) speedup across seven DNNs (13 variants from different RELU approximations) on CPUs, while maintaining inference accuracy.

The RNS-CKKS scheme in Fully Homomorphic Encryption (FHE) supports crucial features for privacy-preserving machine learning, such as fixed-point arithmetic and SIMD-style vectorization. Yet, managing the escalation of ciphertext scales from homomorphic multiplications, which risks capacity overflow, along with bootstrapping, presents significant challenges. These complexities are exacerbated by the need to efficiently handle scale and bootstrapping at compile time while ensuring rapid encrypted inference.

Fully Homomorphic Encryption (FHE) facilitates computations on encrypted data without requiring access to the decryption key, offering substantial privacy benefits for deploying neural network applications in sensitive sectors such as healthcare and finance. Nonetheless, programming these applications within the FHE framework is complex and demands extensive cryptographic expertise to guarantee correctness, performance, and security. In this paper, we present ANT-ACE, a production-quality, open-source FHE compiler designed to automate neural network inference on encrypted data. ANT-ACE accepts ONNX models and generates C/C++ programs, leveraging its custom open-source FHE library. We explore the design challenges encountered in the development of ANT-ACE, which is engineered to support a variety of input formats and architectures across diverse FHE schemes through a novel Intermediate Representation (IR) that facilitates multiple levels of abstraction. Comprising 44,000 lines of C/C++ code, ANT-ACE efficiently translates ONNX models into C/C++ programs for encrypted inference on CPUs, specifically utilizing the RNS-CKKS scheme. Preliminary evaluations on a single CPU indicate that ANT-ACE achieves significant speed enhancements in ResNet models, surpassing expert manual implementations and fulfilling our design goals.

Practical encrypted neural network inference under the CKKS fully homomorphic encryption (FHE) scheme relies heavily on accelerating two key kernel operations: Matrix-Vector Multiplication (MVM) and Convolution (Conv). However, existing solutions—such as expert-tuned libraries and domain-specific languages—are designed in an ad hoc manner, leading to significant inefficiencies caused by excessive rotations. We introduce MKR, a novel composition-based compiler approach that optimizes MVM and Conv kernel operations for DNN models under CKKS within a unified framework. MKR decomposes each kernel into composable units, called MetaKernels , to enhance SIMD parallelism within ciphertexts (via horizontal batching) and computational parallelism across them (via vertical batching). Our approach tackles previously unaddressed challenges, including reducing rotation overhead through a rotation-aware cost model for data packing, while also ensuring high slot utilization, uniform handling of inputs with arbitrary sizes, and compatibility with the output tensor layout. Implemented in a production-quality FHE compiler, MKR achieves inference time speedups of 10.08×−185.60× for individual MVM and Conv kernels and 1.75×−11.84× for end-to-end inference compared to a state-of-the-art FHE compiler. Moreover, MKR enables homomorphic execution of large DNN models, where prior methods fail, significantly advancing the practicality of FHE compilers.

Remote sensing images with high temporal and spatial resolutions have broad market demands and various application scenarios. This paper aims to generate high-quality remote sensing image time series for feature mining of the growth quality of traditional Chinese medicine. Spatiotemporal fusion is a flexible method that combines two types of satellite images with high temporal resolution or high spatial resolution to generate high-quality remote sensing images. In recent years, many spatiotemporal fusion algorithms have been proposed, and deep learning-based methods show extraordinary talents in this field. However, the current deep learning-based methods have three problems: 1) most algorithms do not support models with large-scale learnable parameters; 2) the model structure based on convolutional neural networks will bring noise to the image fusion process; 3) current deep learning-based methods ignore some excellent modules in traditional spatiotemporal fusion algorithms. For the above problems and challenges, this paper creatively proposes a new algorithm based on Swin Transformer and linear spectral mixing theory. The algorithm makes full use of the advantages of Swin Transformer in feature extraction, and integrates the unmixing theories into the model based on the self-attention mechanism, which greatly improves the quality of generated images. In the experimental part, the proposed algorithm achieves state-of-the-art results on three well-known public datasets, and has been proved effective and reasonable in ablation study.