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This brief presents the implementation and evaluation of an 8-bit adaptable processor core to be part of the power-throughput-area efficient multimedia oriented reconfigurable architecture reconfigurable array. The design of the processor core was custom implemented in IBM's 90 nm CMOS technology and occupies 0.115 mm2 silicon area with approximately 70% area utilized by core circuits. The processor shows a peak throughput performance of 75 MOPS/mW. Benchmarking results show estimated throughputs of 9.5, 21.36, 39.78, 170.88, and 4.54 MSamples/s for variants of 2-D discrete cosine transform (DCT), 4 × 4 H.264 integer transform, and 2-D discrete wavelet transform, respectively. Our analysis shows that the proposed design provides approximately 4-8 times higher throughput for 2-D DCT when compared against popular architectures.

In this work, we have analyzed the effects of variability, due to random dopant fluctuation (RDF), on the timing characteristics of flip-flops for the future technology generations of 25, 18, and 13 nm, based on extensive Monte Carlo simulations. The results show that RDF has a significant impact on all of the timing parameters and that these parameters do not follow a normal distribution; in particular, they are skewed and exhibit a large tail. Moreover, the dispersion and skewness of the timing parameters increase with technology scaling. The study of the exact shape of these distributions, especially in the tail section, is of fundamental importance in the design and modeling of high-performance, reliable, and economically feasible circuits. In this paper, the distribution tails are estimated based on simulation data, with the aid of statistical nonparametric probability density functions, and it has been found that timing distributions can better be represented by certain nonparametric distributions, in particular Pearson and Johnson systems. The use of these representations during the statistical static timing analysis will provide more accurate results as compared with the normal approximation of distributions and will eventually reduce the probability of yield loss.