Negative bias temperature instability (NBTI) has become a critical reliability phenomena in advanced CMOS technology. In this paper, we propose an analytical temperature-aware dynamic NBTI model, which can be used in two circuit operation cases: executing tasks with different temperatures, and switching between active and standby mode. A PMOS Vth degradation model and a digital circuits' temporal performance degradation estimation method are developed based on our NBTI model. The simulation results show that: 1) the execution of a low temperature task can decrease ΔVth due to NBTI by 24.5%; 2) switching to standby mode can decrease ΔVth by 52.3%; 3) for ISCAS85 benchmark circuits, the delay degradation can decrease significantly if the circuit execute low temperature task or switch to standby mode; 4) we have also observed the execution time's ratio of different tasks and the ratio of active to standby time both have a considerable impact on NBTI effect.

Traditional pipeline methods for task-oriented dialogue systems are designed individually and expensively. Existing memory augmented end-to-end methods directly map the inputs to outputs and achieve promising results. However, the most existing end-to-end solutions store the dialogue history and knowledge base (KB) information in the same memory and represent KB information in the form of KB triples, making the memory reader's reasoning on the memory more difficult, which makes the system difficult to retrieve the correct information from the memory to generate a response. Some methods introduce many manual annotations to strengthen reasoning. To reduce the use of manual annotations, while strengthening reasoning, we propose a hierarchical memory model (HM2Seq) for task-oriented systems. HM2Seq uses a hierarchical memory to separate the dialogue history and KB information into two memories and stores KB in KB rows, then we use memory rows pointer combined with an entity decoder to perform hierarchical reasoning over memory. The experimental results on two publicly available task-oriented dialogue datasets confirm our hypothesis and show the outstanding performance of our HM2Seq by outperforming the baselines.