Runtime Design Space Exploration and Mapping of DCNNs for the Ultra-Low-Power Orlando SoC

Abstract

Recent trends in deep convolutional neural networks (DCNNs) impose hardware accelerators as a viable solution for computer vision and speech recognition. The Orlando SoC architecture from STMicroelectronics targets exactly this class of problems by integrating hardware-accelerated convolutional blocks together with DSPs and on-chip memory resources to enable energy-efficient designs of DCNNs. The main advantage of the Orlando platform is to have runtime configurable convolutional accelerators that can adapt to different DCNN workloads. This opens new challenges for mapping the computation to the accelerators and for managing the on-chip resources efficiently. In this work, we propose a runtime design space exploration and mapping methodology for runtime resource management in terms of on-chip memory, convolutional accelerators, and external bandwidth. Experimental results are reported in terms of power/performance scalability, Pareto analysis, mapping adaptivity, and accelerator utilization for the Orlando architecture mapping the VGG-16, Tiny-Yolo(v2), and MobileNet topologies.