As deep learning algorithms are widely adopted, an increasing number of them positioned in embedded application domains with strict reliability constraints. The expenditure significant resources to satisfy performance requirements neural network accelerators has thinned out the margins for delivering safety applications, thus precluding adoption conventional fault tolerance methods. potential e...

We present a dynamic network rewiring (DNR) method to generate pruned deep neural (DNN) models that both are robust against adversarially generated images and maintain high accuracy on clean images. In particular, the disclosed DNR training is based unified constrained optimization formulation using novel hybrid loss function merges sparse learning with adversarial training. This strategy dynam...

Heterogeneous computing systems (HCSs), which consist of various processing elements (PEs) that vary in their ability, are usually facilitated by the network-on-chip (NoC) to interconnect its components. The emerging point-to-point NoCs support single-cycle-multi-hop transmission, reduce or eliminate latency dependence on distance, addressing scalability concern raised high for long-distance tr...