In recent years, Convolutional Neural Network (CNN) has been widely used in robotics, which has dramatically improved the perception and decision-making ability of robots. A series of CNN accelerators have been designed to implement energy-efficient CNN on embedded systems. However, despite the high energy efficiency on CNN accelerators, it is difficult for robotics developers to use it. Since the various functions on the robot are usually implemented independently by different developers, simultaneous access to the CNN accelerator by these multiple independent processes will result in hardware resources conflicts.To handle the above problem, we propose an INterruptible CNN Accelerator (INCA) to enable multi-tasking on CNN accelerators. In INCA, we propose a Virtual-Instruction-based interrupt method (VI method) to support multi-task on CNN accelerators. Based on INCA, we deploy the Distributed Simultaneously Localization and Mapping (DSLAM) on an embedded FPGA platform. We use CNN to implement two key components in DSLAM, Feature-point Extraction (FE) and Place Recognition (PR), so that they can both be accelerated on the same CNN accelerator. Experimental results show that, compared to the layer-by-layer interrupt method, our VI method reduces the interrupt respond latency to 1%.

Multi-Robot Exploration (MR-Exploration) that provides the location and map is a basic task for many multi-robot applications. Recent researches introduce Convolutional Neural Network (CNN) to critical components in MR-Exploration, like Feature-point Extraction (FE) and Place Recognition (PR), to improve the system performance. Such CNN-based MR-Exploration requires running multiple CNN models simultaneously, together with complex post-processing algorithms, greatly challenges the hardware platforms, which are usually embedded systems. Previous researches have shown that FPGA is a good candidate for CNN processing on embedded platforms. But such accelerators usually process different models sequentially, lacking the ability to schedule multiple tasks at runtime. Furthermore, post-processing of CNNs in FE is also computation consuming and becomes the system bottleneck after accelerating the CNN models. To handle such problems, we propose an INterruptible CNN Accelerator for Multi-Robot Exploration (INCAME) framework for rapid deployment of robot applications on FPGA. In INCAME, we propose a virtual-instruction-based interrupt method to support multi-task on CNN accelerators. INCAME also includes hardware modules to accelerate the post-processing of the CNN-based components. Experimental results show that INCAME enables multi-task scheduling on the CNN accelerator with negligible performance degradation (0.3%). With the help of multi-task supporting and post-processing acceleration, INCAME enables embedded FPGA to execute MR-Exploration in real time (20 fps).