  FPGAs are commonly used to accelerate domain-specific algorithmic
implementations, as they can achieve impressive performance boosts, are
reprogrammable and exhibit minimal power consumption. In this work, the
SqueezeNet DCNN is accelerated using an SoC FPGA in order for the offered
object recognition resource to be employed in a robotic application.
Experiments are conducted to investigate the performance and power consumption
of the implementation in comparison to deployment on other widely-used
computational systems.
