Expanding a robot's life: Low power object recognition via FPGA-based DCNN deployment
This work addresses power efficiency for robotic applications, but it is incremental as it applies an existing method to a new hardware setup.
The authors tackled the problem of enabling low-power object recognition for robots by deploying the SqueezeNet DCNN on an SoC FPGA, achieving performance and power consumption improvements compared to other computational systems.
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.