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Modern network-on-chip (NoC) systems face reliability issues due to process and environmental variations. The power supply noise (PSN) in the power delivery network of a NoC plays a key role in determining reliability. PSN leads to voltage droop, which can cause timing errors in the NoC. This paper makes a novel contribution towards formally analyzing PSN in NoC systems. We present a probabilistic model checking approach to observe the PSN in a generic 2x2 mesh NoC with a uniform random traffic load. Key features of PSN are measured at the behavioral level. To tackle state explosion, we apply incremental abstraction techniques, including a novel probabilistic choice abstraction, based on observations of NoC behavior. The Modest Toolset is used for probabilistic modeling and verification. Results are obtained for several flit injection patterns to reveal their impacts on PSN. Our analysis finds an optimal flit pattern generation with zero probability of PSN events and suggests spreading flits rather than releasing them in consecutive cycles in order to minimize PSN.
Stimulation of target neuronal populations using optogenetic techniques during specific sleep stages has begun to elucidate the mechanisms and effects of sleep. To conduct closed-loop optogenetic sleep studies in untethered animals, we designed a ful
The relationship between topology and network throughput of arbitrarily-connected mesh networks is studied. Taking into account nonlinear channel properties, it is shown that throughput decreases logarithmically with physical network size with minor dependence on network ellipticity.
This paper presents the design, implementation and evaluation of In-N-Out, a software-hardware solution for far-field wireless power transfer. In-N-Out can continuously charge a medical implant residing in deep tissues at near-optimal beamforming pow
Voice traffic prediction is significant for network deployment optimization thus to improve the network efficiency. The real entropy based theorectical bound and corresponding prediction models have demonstrated their success in mobility prediction.
The resource constraints and accuracy requirements for Internet of Things (IoT) memory chips need three-dimensional (3D) monolithic integrated circuits, of which the increasing stack layers (currently more than 176) also cause excessive energy consum