The progress of VLSI technology is facing two limiting factors: power and variation. Minimizing clock network size can lead to reduced power consumption, less power supply noise, less number of clock buffers and therefore less vulnerability to variations. Previous works on clock network minimization are mostly focused on clock routing and the improvements are often limited by the input register placement. In this work, we propose to navigate registers in cell placement for further clock network size reduction. To solve the conflict between clock network minimization and traditional placement goals, we suggest the following techniques in a quadratic placement framework: (1) Manhattan ring based register guidance; (2) center of gravity constraints for registers; (3) pseudo pin and net; (4) register cluster contraction. These techniques work for both zero skew and prescribed skew designs in both wirelength driven and timing driven placement. Experimental results show that our method can reduce clock net wirelength by 16%~33% with no more than 0.5% increase on signal net wirelength compared with conventional approaches.
The development of Earth observation (EO) technology has made the volume of remote sensing data archiving continually larger, but the knowledge hidden in massive remote sensing images has not been fully exploited. Through indepth research on the artificial intelligence (AI)-based knowledge discovery approaches from remote sensing images, we divided them into four typical types according to their development stage, including rule-based approaches, data-driven approaches, reinforcement learning approaches, and ensemble methods. The basic principles, typical applications, advantages, and disadvantages have been detailed for commonly used algorithms within each category. Conclusions include the following: (a) Rule-based, data-driven and reinforcement learning algorithms form a trilogy from knowledge to data, and to capabilities. (b) Rule-based data mining algorithms can provide prior knowledge for data-driven approaches, the knowledge discovered by data-driven models can be as an important complement to expert knowledge and rule sets, and reinforcement learning approaches can effectively make up for the lack of training samples or small training sample in data-driven models. (c) The traditional data-driven machine learning approaches and their ensemble methods are the current and may be the future mainstream methods for large regional and even global scale long time series remote sensing data mining and analysis, and improving their computing efficiency is the key research direction.(d) Deep learning, deep reinforcement learning, transfer learning, and an ensemble approach of the three may be the main means for small-area scope, short time series, and key geoscience information extraction from remote sensing images within a long time of the future.
Significance
Antagonistic pleiotropy (AP) is a prevailing theory of the evolution of aging; however, it lacks direct experimental evidence at an individual gene level. We performed unbiased translatome analyses of
Caenorhabditis elegans
recovering from starvation and identified that the
trl-1
gene hidden in a pseudogene generates proteinaceous products upon refeeding. Compared with wild-type animals,
trl-1
mutants increased brood sizes, shortened the animals’ lifespan, and specifically impaired germline deficiency–induced longevity. The TRL-1 protein undergoes liquid–liquid phase separation, through which TRL-1 granules recruit vitellogenin messenger RNA and inhibit its translation. These results provide evidence that
trl-1
regulates the reproduction–longevity tradeoff by optimizing nutrient production for the next generation, thereby supporting the AP theory of aging at the single-gene level.
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