Аңдатпа
optimization of per-layer or/and total inference time without accuracy loss in convolutional neural networks (CNNs) is significantly crucial in resource-constrained Edge-AI devices and embedded systems. To do this, this work 1) introduces a CNN inference micro-benchmark (mbNet) for performance analysis and optimization and 2) proposes a simple yet effective performance model for adaptive kernel selection to optimize per-layer CNN inference time. Considering the convolutional layer is the core part of CNNs, the two mainstream convolutional strategies of unrolling based convolution (UNROLL) and direct convolution (DIRECT) are adopted/implemented, compared and analyzed in terms of per-layer convolutional time. Using the obtained data from our mbNet, we build an accurate and interpretable tree-based performance model, with which our adaptive kernel selection approach shows significant convolutional performance improvement up to $5.4\times$ speedup (on average, $2.7\times$ and $1.6\times$ speedup, compared to the default UNROLL and DIRECT respectively).