Fully homomorphic encryption (FHE) allows operations to be performed directly on encrypted data, and has attracted massive attention in data security scenarios. Numerous resource efficient FHE acceleration methods have been proposed, including many on the optimization of modular multiplication (MM), a fundamental operation in FHE, by leveraging Karatsuba multiplication and NTT-friendly moduli for Montgomery MM (MMM). However, FHE is not yet practical due to its significant resource overheads. In this paper, we report an automated Karatsuba decomposition search strategy that drastically improves the efficiency of MM implementation. Our key idea is to integrate NTT-friendly moduli into Karatsuba decomposition within parallel MMM, and incorporate optimization features such as truncated multiplication ⌊AB/R⌋ and modular multiplicationABmodR. After a careful analysis on the optimization space, we propose an automated Karatsuba decomposition optimization search algorithm based on a greedy strategy to enhance efficiency and effectiveness. Theoretical analysis shows that, under the mainstream NTT-friendly modulus conditions, the optimized 2, 3, 4-term Karatsuba decomposition schemes achieve an average area reduction of 18% for MMM over the basic Karatsuba method and 57% over the classical Schoolbook method. Furthermore, hardware implementations on FPGA demonstrate 29% to 79% improvement, with an average of 59%, in Area/Throughput compared to the state-of-the-art implementations.

Accurately identifying potential drug-target interactions (DTIs) is a critical step in drug discovery. Multiple heterogeneous biological data provide abundant features for DTI prediction. Many computational methods have been proposed based on these data. However, most of these methods either extract features from sequences or from networks, utilizing only one aspect of the characteristics of drugs and targets, neglecting the complementary information between these two types of features. In fact, integrating different types of features will provide more valuable information for DTI prediction. In this article, we propose a novel method to improve the predictive capability for DTIs, named MFCADTI, by integrating multi-source feature through cross-attention mechanisms. The method extracts network topological features from the heterogeneous network and attribute features from sequences of drugs and targets. Considering the complementarity and heterogeneity between network and attribute features, cross-attention mechanisms are used to integrate the network and attribute features of drugs and targets. To capture the correlations between drugs and targets, cross-attention is used to learn the interaction features of each drug-target pair. We evaluate MFCADTI on two datasets and experimental results demonstrate a significant improvement in the performance of MFCADTI compared to state-of-the-art methods. Finally, case studies illustrate that MFCADTI is an effective DTI prediction way that provides valuable guidance for drug development. The data and source code used in this study are available at: https://github.com/Dejavun/MFCADTI .