INCAME: Interruptible CNN Accelerator for Multirobot Exploration

Abstract

Multirobot exploration (MR-Exploration) is a primary task providing the location and map for many multirobot applications. To improve system performance, convolutional neural network (CNN) is introduced by recent researches into critical components in MR-Exploration, such as feature-point extraction (FE) and place recognition (PR). This CNN-based MR-Exploration needs to simultaneously run multiple CNN models and complex postprocessing algorithms. This significantly challenges the hardware platforms of embedded systems. Previous researches reveal that an FPGA is ideal for CNN processing on embedded platforms. Such accelerators usually process different models in sequence, while they cannot schedule multiple tasks at runtime. Furthermore, the postprocessing of CNNs is computationally intensive and becomes the bottleneck of the whole system. To handle such problems, we propose an interruptible CNN accelerator for multirobot exploration (INCAME) framework to rapidly deploy the robot applications on FPGAs. In INCAME, we propose an interrupt method based on virtual instructions to support multitasking on CNN accelerators. INCAME also includes hardware modules for accelerating the postprocessing of the CNN-based components. Organically, it integrates the postprocessing and CNN backbone by sharing memory. Experimental results reveal that INCAME enables multitask scheduling on the CNN accelerator with negligible performance degradation (0.3%). INCAME enables embedded FPGAs to perform MR-Exploration in real time (20 fps) via the multitask support and postprocessing acceleration.