Detecting And Mitigating Fault In Sparsity Computation In Deep Neural Network

Abstract: Computations in processing elements (PEs) for executing a deep neural network (DNN) may be accelerated based on sparsity. A compressed activation operand and a compressed weight operand may be stored. The compressed activation operand includes one or more nonzero activations in an activation operand. The compressed weight operand includes one or more nonzero weights in a weight operand. A sparsity module associated with the PE may generate a bitmap based on an activation sparsity vector of the activation operand and a weight sparsity vector of the weight operand. The sparsity module identifies a nonzero activation (or a nonzero weight) from the compressed activation operand (or the compressed weight operand) based on the bitmap. The sparsity module may detect a fault in identifying the nonzero activation or the nonzero weight based on the number of one or more nonzero elements in the bitmap. The sparsity module may further mitigate the fault.

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