Variations-aware low-power design with voltage scaling

We present a new methodology which takes into consideration the effect of Within-Die (WID) process variations on a low-voltage parallel system. We show that in the presence of process variations one should use a higher supply voltage than would otherwise be predicted to minimize the power consumption of a parallel systems. Previous analyses, which ignored WID process variations, provide a lower non-optimal supply voltage which can underestimate the energy/operation by 8.2X. We also present a novel technique to limit the effect of temperature variations in a parallel system. As temperatures increases, the scheme reduces the power increase by 43 % allowing the system to remain at it’s optimal supply voltage across different temperatures

STATISTICAL PROCESSING OF LARGE IMAGE SEQUENCES (Accepted for publication in IEEE Trans. Image Processing)

Abstract—The dynamic estimation of large-scale stochastic image sequences, as frequently encountered in remote sensing, is important in a variety of scientific applications. However, the size of such images makes conventional dynamic estimation methods, for example the Kalman and related filters, impractical. In this paper we present an approach that emulates the Kalman filter, but with considerably reduced computational and storage requirements. Our approach is illustrated in the context of a 512 × 512 image sequence of ocean surface temperature. The static estimation step, the primary contribution here, uses a mixture of stationary models to accurately mimic the effect of a nonstationary prior, simplifying both computational complexity and modelling. Our approach provides an efficient, stable, positive-definite model which is consistent with the given correlation structure. Thus the methods of this paper may find application in modelling and single-frame estimation. I

Variations-aware low-power design with voltage scaling

We present a new methodology which takes into consideration the effect of Within-Die (WID) process variations on a low-voltage parallel system. We show that in the presence of process variations one should use a higher supply voltage than would otherwise be predicted to minimize the power consumption of a parallel systems. Previous analyses, which ignored WID process variations, provide a lower non-optimal supply voltage which can underestimate the energy/operation by 8.2X. We also present a novel technique to limit the effect of temperature variations in a parallel system. As temperatures increases, the scheme reduces the power increase by 43 % allowing the system to remain at it’s optimal supply voltage across different temperatures

Variations-Aware Low-Power Design and Block Clustering With Voltage Scaling

Abstract—We present a new methodology which takes into consideration the effect of within-die (WID) process variations on a low-voltage parallel system. We show that in the presence of process variations one should use a higher supply voltage than would otherwise be predicted to minimize the power consumption of a parallel systems. Previous analyses, which ignored WID process variations, provide a lower nonoptimal supply voltage which can underestimate the energy/operation by 8.2. We also present a novel technique to limit the effect of temperature variations in a parallel system. As temperatures increases, the scheme reduces the power increase by 43 % allowing the system to remain at it’s optimal supply voltage across different temperatures. To further limit the effect of variations, and allow for a reduced power consumption, we analyzed the effects of clustering. It was shown that providing different voltages to each cluster can provide a further 10 % reduction in energy/operation to a low-voltage parallel system, and that the savings by clustering increase as technology scales. Index Terms—Low-voltage, parallel systems, process variations