We investigate per-flow flit and packet worst-case delay bounds in on-chip wormhole networks. Such investigation is essential in order to provide guarantees under worst-case conditions in cost-constrained systems, as required by many hard real-time embedded applications. We first propose analysis models for flow control, link and buffer sharing. Based on these analysis models, we obtain an open-ended service analysis model capturing the combined effect of flow control, link and buffer sharing. With the service analysis model, we compute equivalent service curves for individual flows, and then derive their flit and packet delay bounds. Our experimental results verify that our analytical bounds are correct and tight.

A frontal and realistic face image was synthesized from a single profile face image. It has a wide range of applications in face recognition. Although the frontal face method based on deep learning has made substantial progress in recent years, there is still no guarantee that the generated face has identity consistency and illumination consistency in a significant posture. This paper proposes a novel pixel-based feature regression generative adversarial network (PFR-GAN), which can learn to recover local high-frequency details and preserve identity and illumination frontal face images in an uncontrolled environment. We first propose a Reslu block to obtain richer feature representation and improve the convergence speed of training. We then introduce a feature conversion module to reduce the artifacts caused by face rotation discrepancy, enhance image generation quality, and preserve more high-frequency details of the profile image. We also construct a 30,000 face pose dataset to learn about various uncontrolled field environments. Our dataset includes ages of different races and wild backgrounds, allowing us to handle other datasets and obtain better results. Finally, we introduce a discriminator used for recovering the facial structure of the frontal face images. Quantitative and qualitative experimental results show our PFR-GAN can generate high-quality and high-fidelity frontal face images, and our results are better than the state-of-art results.

Credit-based router-to-router flow control is one main link-level flow control mechanism proposed for Networks on Chip (NoCs). Based on network calculus, we analyze its performance and optimal buffer size. To model the feedback control behavior due to credits, we introduce a virtual network service element called flow controller. Then we derive its service curve, and further the system service curve. In addition, we give and prove a theorem that determines the optimal buffer size guaranteeing the maximum system service curve. Moreover, assuming the latency-rate server model for routers, we give closed-form formulas to calculate the flit delay bound and optimal buffer size. Our experiments with real on-chip traffic traces validate that our analysis is correct; delay bounds are tight and the optimal buffer size is exact.