Pruning Convolution Neural Network (SqueezeNet) for Efficient Hardware Deployment
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In recent years, deep learning models have become popular in the real-time embedded application, but there are many complexities for hardware deployment because of limited resources such as memory, computational power, and energy. Recent research in the field of deep learning focuses on reducing the model size of the Convolution Neural Network (CNN) by various compression techniques like Architectural compression, Pruning, Quantization, and Encoding (e.g., Huffman encoding). Network pruning is one of the promising technique to solve these problems.
This thesis proposes methods to prune the convolution neural network (SqueezeNet) without introducing network sparsity in the pruned model.
This thesis proposes three methods to prune the CNN to decrease the model size of CNN without a significant drop in the accuracy of the model.
1: Pruning based on Taylor expansion of change in cost function Delta C.
2: Pruning based on L2 normalization of activation maps.
3: Pruning based on a combination of method 1 and method 2.
The proposed methods use various ranking methods to rank the convolution kernels and prune the lower ranked filters afterwards SqueezeNet model is fine-tuned by backpropagation. Transfer learning technique is used to train the SqueezeNet on the CIFAR-10 dataset. Results show that the proposed approach reduces the SqueezeNet model by 72% without a significant drop in the accuracy of the model (optimal pruning efficiency result). Results also show that Pruning based on a combination of Taylor expansion of the cost function and L2 normalization of activation maps achieves better pruning efficiency compared to other individual pruning criteria and most of the pruned kernels are from mid and high-level layers. The Pruned model is deployed on BlueBox 2.0 using RTMaps software and model performance was evaluated.