Perturbation-Based Computing for Next-Generation Embedded IT Targeted at Emerging Nanoelectronics

This paper makes the case for 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.