Bufferless networks-on-chip (NoCs) with deflection routing are a promising approach for saving area and power in the communication fabric of many-core processors. However, deflection routing imposes fundamental limitations on the number of input and outports per router, which compromises the NoCs' ability to adapt to permanent link failures due to wearout. The susceptibility to wearout of modern NoCs has increased due to deeper sub-micro CMOS process technology. In this paper, we propose scheduled deflections for bufferless NoCs (SchedNoC), a framework that prolongs the lifetime of bufferless NoCs via strategic time-multiplexing of links, which allows links to fail arbitrarily while ensuring correct network operation. We show that SchedNoC can increase the NoCs' lifetime by 62.5%- 71.8% at only 4%-15% performance degradation compared to conventional deflection routing.

We develop a conditional generative model for longitudinal image datasets based on sequential invertible neural networks. Longitudinal image acquisitions are common in various scientific and biomedical studies where often each image sequence sample may also come together with various secondary (fixed or temporally dependent) measurements. The key goal is not only to estimate the parameters of a deep generative model for the given longitudinal data, but also to enable evaluation of how the temporal course of the generated longitudinal samples are influenced as a function of induced changes in the (secondary) temporal measurements (or events). Our proposed formulation incorporates recurrent subnetworks and temporal context gating, which provide a smooth transition in a temporal sequence of generated data that can be easily informed or modulated by secondary temporal conditioning variables. We show that the formulation works well despite the smaller sample sizes common in these applications. Our model is validated on two video datasets and a longitudinal Alzheimer's disease (AD) dataset for both quantitative and qualitative evaluations of the generated samples. Further, using our generated longitudinal image samples, we show that we can capture the pathological progressions in the brain that turn out to be consistent with the existing literature, and could facilitate various types of downstream statistical analysis.

Visual relationships provide higher-level information of objects and their relations in an image - this enables a semantic understanding of the scene and helps downstream applications. Given a set of localized objects in some training data, visual relationship detection seeks to detect the most likely "relationship" between objects in a given image. While the specific objects may be well represented in training data, their relationships may still be infrequent. The empirical distribution obtained from seeing these relationships in a dataset does not model the underlying distribution well - a serious issue for most learning methods. In this work, we start from a simple multi-relational learning model, which in principle, offers a rich formalization for deriving a strong prior for learning visual relationships. While the inference problem for deriving the regularizer is challenging, our main technical contribution is to show how adapting recent results in numerical linear algebra lead to efficient algorithms for a factorization scheme that yields highly informative priors. The factorization provides sample size bounds for inference (under mild conditions) for the underlying [object, predicate, object] relationship learning task on its own and surprisingly outperforms (in some cases) existing methods even without utilizing visual features. Then, when integrated with an end-to-end architecture for visual relationship detection leveraging image data, we substantially improve the state-of-the-art.