Sanjukta Bhanja

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Switching activity estimation is an important aspect of power estimation at circuit level. Switching activity in a node is temporally correlated with its previous value and is spatially correlated with other nodes in the circuit. It is important to capture the effects of such correlations while estimating the switching activity of a circuit. In this paper,(More)
We propose a novel single event fault/error model based on logic induced fault encoded directed acyclic graph (LIFE-DAG) structured probabilistic Bayesian networks, capturing all spatial dependencies induced by the circuit logic. The detection probabilities also act as a measure of soft error susceptibility (an increased threat in nanodomain logic block)(More)
We propose a novel, non-simulative, probabilistic model for switching activity in sequential circuits, capturing both spatio-temporal correlations at internal nodes and higher order temporal correlations due to feedback. This model, which we refer to as the temporal dependency model (TDM), can be constructed from the logic structure and is shown to be a(More)
In this paper, different circuit arrangements of quantum-dot cellular automata (QCA) are proposed for the so-called coplanar crossing. These arrangements exploit the majority voting properties of QCA to allow a robust crossing of wires on the Cartesian plane. This is accomplished using enlarged lines and voting. Using a Bayesian network (BN) based(More)
We propose a novel fault/error model based on a graphical probabilistic framework. We arrive at the Logic Induced Fault Encoded Directed Acyclic Graph (LIFE-DAG) that is proven to be a Bayesian network, capturing all spatial dependencies induced by the circuit logic. Bayesian Networks are the minimal and exact representation of the joint probability(More)
We represent switching activity in VLSI circuits using a graphical probabilistic model based on Cascaded Bayesian Networks (CBN’s). We develop an elegant method for maintaining probabilistic consistency in the interfacing boundaries across the CBN’s during the inference process using a tree-dependent (TD) probability distribution function. A tree-dependent(More)