  The ever-growing cost of both training and inference for state-of-the-art
neural networks has brought literature to look upon ways to cut off resources
used with a minimal impact on accuracy. Using lower precision comes at the cost
of negligible loss in accuracy. While training neural networks may require a
powerful setup, deploying a network must be possible on low-power and
low-resource hardware architectures. Reconfigurable architectures have proven
to be more powerful and flexible than GPUs when looking at a specific
application. This article aims to assess the impact of mixed-precision when
applied to neural networks deployed on FPGAs. While several frameworks exist
that create tools to deploy neural networks using reduced-precision, few of
them assess the importance of quantization and the framework quality. FINN and
Brevitas, two frameworks from Xilinx labs, are used to assess the impact of
quantization on neural networks using 2 to 8 bit precisions and weights with
several parallelization configurations. Equivalent accuracy can be obtained
using lower-precision representation and enough training. However, the
compressed network can be better parallelized allowing the deployed network
throughput to be 62 times faster. The benchmark set up in this work is
available in a public repository (https://github.com/QDucasse/nn benchmark).
