Accelerating Inference of 3D-CNN on ARM Many-core CPU via Hierarchical Model Partition

Many applications such as biomedical analysis and scientific data analysis involve analyzing volumetric data. This spawns huge demand for 3D CNN. Although accelerators such as GPU may provide higher throughput on deep learning applications, they may not be available in all scenarios. CPU, especially many-core CPU, remains an attractive choice for deep learning in many scenarios. In this paper, we propose a inference solution that targets on the emerging ARM many-core CPU platform. A hierarchical partition approach is claimed to accelerate 3D-CNN inference by exploiting characteristics of memory and cache on ARM many-core CPU.