  Deep Learning is moving to edge devices, ushering in a new age of distributed
Artificial Intelligence (AI). The high demand of computational resources
required by deep neural networks may be alleviated by approximate computing
techniques, and most notably reduced-precision arithmetic with coarsely
quantized numerical representations. In this context, Bonseyes comes in as an
initiative to enable stakeholders to bring AI to low-power and autonomous
environments such as: Automotive, Medical Healthcare and Consumer Electronics.
To achieve this, we introduce LPDNN, a framework for optimized deployment of
Deep Neural Networks on heterogeneous embedded devices. In this work, we detail
the quantization engine that is integrated in LPDNN. The engine depends on a
fine-grained workflow which enables a Neural Network Design Exploration and a
sensitivity analysis of each layer for quantization. We demonstrate the engine
with a case study on Alexnet and VGG16 for three different techniques for
direct quantization: standard fixed-point, dynamic fixed-point and k-means
clustering, and demonstrate the potential of the latter. We argue that using a
Gaussian quantizer with k-means clustering can achieve better performance than
linear quantizers. Without retraining, we achieve over 55.64\% saving for
weights' storage and 69.17\% for run-time memory accesses with less than 1\%
drop in top5 accuracy in Imagenet.
