  As edge applications using convolutional neural networks (CNN) models grow,
it is becoming necessary to introduce dedicated hardware accelerators in which
network parameters and feature-map data are represented with limited precision.
In this paper we propose a novel quantization algorithm for energy-efficient
deployment of the hardware accelerators. For weights and biases, the optimal
bit length of the fractional part is determined so that the quantization error
is minimized over their distribution. For feature-map data, meanwhile, their
sample distribution is well approximated with the generalized gamma
distribution (GGD), and accordingly the optimal quantization step size can be
obtained through the asymptotical closed form solution of GGD. The proposed
quantization algorithm has a higher signal-to-quantization-noise ratio (SQNR)
than other quantization schemes previously proposed for CNNs, and even can be
more improved by tuning the quantization parameters, resulting in efficient
implementation of the hardware accelerators for CNNs in terms of power
consumption and memory bandwidth.
