For the state-of-the-art slim CNN model made for the embedded platform, MobileNetV2, CompactNet achieves up to a 1.8x kernel computation speedup with equal or even higher accuracy for image classification tasks on the ImageNet dataset, which outperforms other successful CNN optimizing techniques. We evaluate our work on two platforms of a mobile ARM CPU and a machine learning accelerator NPU (Cambricon-1A ISA) on a Huawei Mate10 smartphone. Guided by a simulator of the target platform, CompactNet progressively trims a pre-trained network by removing certain redundant filters until the target speedup is reached and generates an optimal platform-specific model while maintaining the accuracy. This work proposes a solution, called CompactNet, which automatically optimizes a pre-trained CNN model on a specific resource-limited platform given a specific target of inference speedup. Meanwhile, due to the complex model structures against strict latency and memory restriction, the implementation of CNN models on the resource-limited platforms is becoming more challenging. ![]() ![]() Convolutional Neural Network (CNN) based Deep Learning (DL) has achieved great progress in many real-life applications.
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