Abstract-Optical Network-on-Chip (ONoC) is an emerging technology considered as one of the key solutions for future generation on-chip interconnects. However, silicon photonic devices in ONoC are highly sensitive to temperature variation, which leads to a lower efficiency of Vertical-Cavity SurfaceEmitting Lasers (VCSELs), a resonant wavelength shift of Microring Resonators (MR), and results in a lower Signal to Noise Ratio (SNR). In this paper, we propose a methodology enabling thermal-aware design for optical interconnects relying on CMOS-compatible VCSEL. Thermal simulations allow designing ONoC interfaces with low gradient temperature and analytical models allow evaluating the SNR.
Nanophotonic is an emerging technology considered as one of the key solutions for future generation on-chip interconnects. However, silicon photonic devices are highly sensitive to temperature variation. Under a given chip activity, this leads to a lower laser efficiency and a drift of wavelengths of optical devices (on-chip lasers and microring resonators (MRs)), which results in a higher Bit Error Ratio (BER). In this paper, we propose to jointly tune the on-chip lasers and and MRs in order to align the wavelengths of the emitted signals with the resonant wavelengths of the MRs. Our method allows significant improvements of the power consumption with regard to the related methods, while meeting the BER requirement. Compared to methods for which laser tuning is not possible, results show that a combined tuning of laser and MRs leads to 53% energy reduction when the uniform chip activity decreases from 20% to 5%. BER-energy tradeoffs have been explored and allow strategies to be defined to minimize either the energy, or the BER. As a key result, we have shown that, under specific chip activities, increasing the laser power consumption allows both energy and BER to be improved. This trend has been observed for a MWSR channel interconnecting 12 interfaces.
Respondent-Driven Sampling (RDS) is a form of link-tracing sampling, a sampling technique for 'hard-to-reach' populations that aims to leverage individuals' social relationships to reach potential participants. While the methodological focus has been restricted to the estimation of population proportions, there is a growing interest in the estimation of uncertainty for RDS as recent findings suggest that most variance estimators underestimate variability. Recently, Baraff et al. (2016) proposed the tree bootstrap method based on resampling the RDS recruitment tree, and empirically showed that this method outperforms current bootstrap methods. However, some findings suggest that the tree bootstrap (severely) overestimates uncertainty. In this paper, we propose the neighbourhood bootstrap method for quantifiying uncertainty in RDS, and empirically show that our method outperforms the tree bootstrap in terms of bias and coverage under realistic RDS sampling assumptions.
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