Diffusion Transformers (DiTs) have demonstrated exceptional performance in high-fidelity image and video generation. To reduce their substantial computational costs, feature caching techniques have been proposed to accelerate inference by reusing hidden representations from previous timesteps. However, current methods often struggle to maintain generation quality at high acceleration ratios, where prediction errors increase sharply due to the inherent instability of long-step forecasting. In this work, we adopt an ordinary differential equation (ODE) perspective on the hidden-feature sequence, modeling layer representations along the trajectory as a feature-ODE. We attribute the degradation of existing caching strategies to their inability to robustly integrate historical features under large skipping intervals. To address this, we propose FoCa (Forecast-then-Calibrate), which treats feature caching as a feature-ODE solving problem. Extensive experiments on image, video generation, and super-resolution tasks demonstrate the effectiveness of FoCa, especially under aggressive acceleration. Without additional training, FoCa achieves near-lossless speedups of 5.50× on FLUX, 6.45× on HunyuanVideo, 3.17× on Inf-DiT, and maintains high quality with a 4.53× speedup on DiT.

The rapid advancement of cloud computing has facilitated the widespread integration of field-programmable gate arrays (FPGAs) into cloud servers to accelerate computing processes. To reduce costs, cloud service providers aim to promote multi-tenant sharing of a single FPGA. In multi-tenant environments, tenants share the power distribution network (PDN), which introduces potential security vulnerabilities. Malicious tenants can conduct remote power analysis attacks by deploying on-chip voltage sensors to detect voltage fluctuations in the PDN. Previous research has focused on enhancing the flexibility and stealthiness of sensors, while insufficient attention has been paid to optimizing sensor parameters for accurate power measurement. This work introduces a phase tuning method for voltage sensors in remote power analysis attacks on AES. The experimental results demonstrate that adjusting the sampling phase enhances the performance of correlation power analysis (CPA) attacks. On average, this method reduces the number of power traces required for CPA attacks by 43.1% and improves the average ranking of correct subkeys by 1.968×. The work reveals novel security threats in multi-tenant FPGAs and contributes to advancing cloud FPGA security research.