Cyclic Sparsely Connected Architectures: From foundations to applications

Deep neural networks (DNNs) are compute-intensive nonlinear mathematical functions that employ matrix/tensor operators at their core to identify temporal and/or spatial correlations within input data. Common techniques, such as pruning, quantization, and compact model design, have been proposed by researchers and extensively utilized by developers to reduce the computation and bulky size of DNNs.