Accelerating CNN-RNN Based Machine Health Monitoring on FPGA

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Abstract
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.

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