Kernel Shape Control for Row-Efficient Convolution on Processing-In-Memory Arrays

Processing-in-memory (PIM) architectures have been highlighted as one of the viable solutions for faster and more power-efficient convolutional neural networks (CNNs) inference. Recently, shift and duplicate kernel (SDK) convolutional weight mapping scheme was proposed, achieving up to 50% throughput improvement over the prior arts. However, the traditional pattern-based pruning methods, which were adopted for row-skipping and computing cycle reduction, are not optimal for the latest SDK mapping due to structural irregularity caused by the shifted and duplicated kernels. To address this issue, we propose a method called kernel shape control (KERNTROL) that aims to promote structural regularity for achieving a high row-skipping ratio and model accuracy. Instead of pruning certain weight elements permanently, KERNTROL controls the kernel shapes through the omission of certain weights based on their mapped columns. In comparison to the latest pattern-based pruning approaches, KERNTROL achieves up to 36.4% improvement in the compression rate, and 38.6% in array utilization with maintaining the original model accuracy.