Exploring scaling limits and computational paradigms for next generation embedded systems

Abstract

It is widely recognized that device and interconnect fabrics at the nanoscale will be characterized by a higher density of permanent defects and increased susceptibility to transient faults. This appears to be intrinsic to nanoscale regimes and fundamentally limits the eventual benefits of the increased device density, i.e., the overheads associated with achieving fault-tolerance may counter the benefits of increased device density -- density-reliability tradeoff. At the same time, as devices scale down one can expect a higher proportion of area to be associated with interconnection, i.e., area is wire dominated. In this work we theoretically explore density-reliability tradeoffs in wire dominated integrated systems. We derive an area scaling model based on simple assumptions capturing the salient features of hierarchical design for high performance systems, along with first order assumptions on reliability, wire area, and wire length across hierarchical levels. We then evaluate overheads associated with using basic fault-tolerance techniques at different levels of the design hierarchy. This, albeit simplified model, allows us to tackle several interesting theoretical questions: (1) When does it make sense to use smaller less reliable devices? (2) At what scale of the design hierarchy should fault tolerance be applied in high performance integrated systems? In the second part of this thesis we explore perturbation-based computational models as a promising choice for implementing next generation ubiquitous information technology on unreliable nanotechnologies. We show the inherent robustness of such computational models to high defect densities and performance uncertainty which, when combined with low manufacturing precision requirements, makes them particularly suitable for emerging nanoelectronics. We propose a hybrid eNano-CMOS perturbation-based computing platform relying on a new style of configurability that exploits the computational model's unique form of unstructured redundancy. We consider the practicality and scalability of perturbation-based computational models by developing and assessing initial foundations for engineering such systems. Specifically, new design and decomposition principles exploiting task specific contextual and temporal scales are proposed and shown to substantially reduce complexity for several benchmark tasks. Our results provide strong evidence for the relevance and potential of this class of computational models when targeted at emerging unreliable nanoelectronics.