Networks-on-Chip constitute the interconnection architecture of future, massively parallel multiprocessors that assemble hundreds to thousands of processing cores on a single chip. Their integration is enabled by ongoing miniaturization of chip manufacturing technologies following Moore's Law. It comes with the downside of the circuit elements' increased susceptibility to failure. Research on fault-tolerant Networks-on-Chip tries to mitigate partial failure and its effect on network performance and reliability by exploiting various forms of redundancy at the suitable network layers. The article at hand reviews the failure mechanisms, fault models, diagnosis techniques, and fault-tolerance methods in on-chip networks, and surveys and summarizes the research of the last ten years. It is structured along three communication layers: the data link, the network, and the transport layers. The most important results are summarized and open research problems and challenges are highlighted to guide future research on this topic.
Abstract:A new cycloartane-type triterpenoid saponin named riparsaponin (1) was isolated from the stem of Homonoia riparia Lour together with six known compounds. The structure of riparsaponin was determined by using NMR and mass spectroscopy and X-ray crystallography techniques. Additionally, riparsaponin has a significant inhibitory effect on xanthine oxidase in vitro, and the IC 50 was 11.16 nmol/mL.
To improve the efficiency of direct solution methods in SPICE-accurate integrated circuit (IC) simulations, preconditioned iterative solution techniques have been widely studied in the past decades. However, it is still an extremely challenging task to develop robust yet efficient general-purpose preconditioning methods that can deal with various types of large-scale IC problems. In this paper, based on recent graph sparsification research we propose circuit-oriented general-purpose support-circuit preconditioning (GPSCP) methods to dramatically improve the sparse matrix solution time and reduce the memory cost during SPICE-accurate IC simulations. By sparsifying the Laplacian matrix extracted from the original circuit network using graph sparsification techniques, general-purpose support circuits can be efficiently leveraged as preconditioners for solving large Jacobian matrices through Krylov-subspace iterations. Additionally, a performance model-guided graph sparsification framework is proposed to help automatically build nearly-optimal GPSCP solvers. Our experiment results for a variety of large-scale IC designs show that the proposed preconditioning techniques can achieve up to 18× runtime speedups and 7× memory reduction in DC and transient simulations when compared to state-of-the-art direct solution methods.
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