Operating deep neural networks (DNNs) on devices with limited resources
requires the reduction of their memory as well as computational footprint.
Popular reduction methods are network quantization or pruning, which either
reduce the word length of the network parameters or remove weights from the
network if they are not needed. In this article we discuss a general framework
for network reduction which we call `Look-Up Table Quantization` (LUT-Q). For
each layer, we learn a value dictionary and an assignment matrix to represent
the network weights. We propose a special solver which combines gradient
descent and a one-step k-means update to learn both the value dictionaries and
assignment matrices iteratively. This method is very flexible: by constraining
the value dictionary, many different reduction problems such as non-uniform
network quantization, training of multiplierless networks, network pruning or
simultaneous quantization and pruning can be implemented without changing the
solver. This flexibility of the LUT-Q method allows us to use the same method
to train networks for different hardware capabilities.Comment: Copyright 2019 IEEE. Personal use of this material is permitted.
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