Deep learning algorithms have two distinct phases. First is a model training phase involves evaluating and modifying weights in a deep learning model using a set of labelled or even unlabeled data. The second phase is inference or scoring, where the trained models are taken as fixed values and deployed…

Today’s largest Field Programmable Gate Array(FPGA) designs can easily take hours to place with no guarantee of routing success. Therefore, it is crucial for the placement tool to know as early as possible whether a design is routable.

In the FPGA design flow, Routability prediction is closely associated with the…

The basic idea of deep learning is to construct a network like synapses in human brain called neuron network, and calculate the network by matrix and vector operations. The prediction accuracy is related to the number of neurons it uses. …

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