Exploring NEURAGHE: A Customizable Template for APSoC-based CNN Inference at the Edge

The NEURAGHE architecture has proved to be a powerful accelerator for Deep Convolutional Neural Networks running on heterogeneous architectures based on Xilinx Zynq-7000 APSoCs. NEURAGHE exploits the processing system and the programmable logic available in these devices, to improve performance through parallelism and to widen the scope of use-cases that can be supported. In this work, we extend the NEURAghe template-based architecture to guarantee design-time scalability to multi-processor SoCs with vastly different cost, size and power envelope such as Xilinx’s Z-7007s, Z-7020 and Z-7045. The proposed architecture achieves state-of-the-art performance and cost effectiveness in all the analyzed configurations, reaching up to 335 GOps/s on the Z-7045.