PCA-driven Hybrid network design for enabling Intelligence at the Edge
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The recent advent of IOT has increased the demand for enabling AI-based edge computing in several applications including healthcare monitoring systems, autonomous vehicles etc. This has necessitated the search for efficient implementations of neural networks in terms of both computation and storage. Although extreme quantization has proven to be a powerful tool to achieve significant compression over full-precision networks, it can result in significant degradation in performance for complex image classification tasks. In this work, we propose a Principal Component Analysis (PCA) driven methodology to design mixed-precision, hybrid networks. Unlike standard practices of using PCA for dimensionality reduction, we leverage PCA to identify significant layers in a binary network which contribute relevant transformations on the input data by increasing the number of significant dimensions. Subsequently, we propose Hybrid-Net, a network with increased bit-precision of the weights and activations of the significant layers in a binary network. We show that the proposed Hybrid-Net achieves over 10 improvement in classification accuracy over binary networks such as XNOR-Net for ResNet and VGG architectures on CIFAR-100 and ImageNet datasets while still achieving upto 94 methodology allows us to move closer to the accuracy of standard full-precision networks by keeping more than half of the network binary. This work demonstrates an effective, one-shot methodology for designing hybrid, mixed-precision networks which significantly improve the classification performance of binary networks while attaining remarkable compression. The proposed hybrid networks further the feasibility of using highly compressed neural networks for energy-efficient neural computing in IOT-based edge devices.
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