  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% reduction in the number of computations, a 52.2% reduction
in the model size, and a 30.6% reduction in the overall memory footprint over a
state-of-the-art keyword-spotting neural network, with negligible loss in
accuracy.
