Exploring NEURAghe: A Customizable Template for APSoC-Based CNN Inference at the Edge

Meloni P.;Loi D.;Deriu G.;Carreras M.;Conti F.;Capotondi A.;Rossi D.

2020-01-01

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Abstract

The NEURAghe architecture has proved to be a powerful accelerator for deep convolutional neural networks running on heterogeneous architectures based on Xilinx Zynq-7000 all programmable system-on-chips. 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 letter, we extend the NEURAghe template-based architecture to guarantee design-time scalability to multiprocessor 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.