Accelerating CNN-RNN Based Machine Health Monitoring on FPGA

Emerging artificial intelligence brings new opportunities for embedded machine health monitoring systems. However, previous work mainly focus on algorithm improvement and ignore the software-hardware co-design. This paper proposes a CNN-RNN algorithm for remaining useful life (RUL) prediction, with hardware optimization for practical deployment. The CNN-RNN algorithm combines the feature extraction ability of CNN and the sequential processing ability of RNN, which shows 23%-53% improvement on the CMAPSS dataset. This algorithm also considers hardware implementation overhead and an FPGA based accelerator is developed. The accelerator adopts kernel-optimized design to utilize data reuse and reduce memory accesses. It enables real-time response and 5.89GOPs/W energy efficiency within small size and cost overhead. The FPGA implementation shows 15× CNN speedup and 9× overall speedup compared with the embedded processor Cortex-A9.