  Deep Neural Networks (DNNs) typically require massive amount of computation
resource in inference tasks for computer vision applications. Quantization can
significantly reduce DNN computation and storage by decreasing the bitwidth of
network encodings. Recent research affirms that carefully selecting the
quantization levels for each layer can preserve the accuracy while pushing the
bitwidth below eight bits. However, without arduous manual effort, this deep
quantization can lead to significant accuracy loss, leaving it in a position of
questionable utility. As such, deep quantization opens a large hyper-parameter
space (bitwidth of the layers), the exploration of which is a major challenge.
We propose a systematic approach to tackle this problem, by automating the
process of discovering the quantization levels through an end-to-end deep
reinforcement learning framework (ReLeQ). We adapt policy optimization methods
to the problem of quantization, and focus on finding the best design decisions
in choosing the state and action spaces, network architecture and training
framework, as well as the tuning of various hyperparamters. We show how ReLeQ
can balance speed and quality, and provide an asymmetric general solution for
quantization of a large variety of deep networks (AlexNet, CIFAR-10, LeNet,
MobileNet-V1, ResNet-20, SVHN, and VGG-11) that virtually preserves the
accuracy (=< 0.3% loss) while minimizing the computation and storage cost. With
these DNNs, ReLeQ enables conventional hardware to achieve 2.2x speedup over
8-bit execution. Similarly, a custom DNN accelerator achieves 2.0x speedup and
energy reduction compared to 8-bit runs. These encouraging results mark ReLeQ
as the initial step towards automating the deep quantization of neural
networks.
