In this paper, we will show some significant results of the routability analysis of bit-serial pipeline datapath designs based on Rent's rule and Donath's observation. Our results show that all of the tested bit-serial benchmarks have Rent exponent of below 0.4, indicating that the average wiring length of the circuit is expected to be independent of the circuit size. This study provides some important implications on the silicon utilization and time-area efficiency of bit-serial pipeline circuits on FPGAs and ASICs.

In many real application scenarios of image segmentation problems involving limited and low-quality data, employing prior information can significantly improve the segmentation result. For example, the shape of the object is a kind of common prior information. In this paper, we introduced a new kind of prior information, which is named by prior distribution. On the basis of nonparametric statistical active contour model, we proposed a novel distribution prior model. Unlike traditional shape prior model, our model is not sensitive to the shapes of object boundary. Using the intensity distribution of objects and backgrounds as prior information can simplify the process of establishing and solving the model. The idea of constructing our energy function is as follows. During the contour curve convergence, while maximizing distribution difference between the inside and outside of the active contour, the distribution difference between the inside/outside of contour and the prior object/background is minimized. We present experimental results on a variety of synthetic and natural images. Experimental results demonstrate the potential of the proposed method that with the information of prior distribution, the segmentation effect and speed can be both improved efficaciously.