A Quality-Oriented Reconfigurable Convolution Engine Using Cross-Shaped Sparse Kernels for Highly-Parallel CNN Acceleration

Computational imaging CNNs are computationally intensive and need complexity reduction to support high-throughput applications. However, conventional compact model reduction tends to degrade image quality severely as models become too shallow. On the other hand, irregular pruning-based techniques induce considerable circuit overheads and imbalanced workloads, especially for highly-parallel acceler...