Ternary Hybrid Neural-Tree Networks for Highly Constrained IoT Applications
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Machine learning-based applications are increasingly prevalent in IoT devices. The power and storage constraints of these devices make it particularly challenging to run modern neural networks, limiting the number of new applications that can be deployed on an IoT system. A number of compression techniques have been proposed, each with its own trade-offs. We propose a hybrid network which combines the strengths of current neural- and tree-based learning techniques in conjunction with ternary quantization, and show a detailed analysis of the associated model design space. Using this hybrid model we obtained a 11.1 in the model size, and a 30.6 state-of-the-art keyword-spotting neural network, with negligible loss in accuracy.
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