Lightweight CNN hardware accelerator using the ternary quantization method for fault diagnosis of CNC machinery

In the context of computer numerical control (CNC) machinery, fault diagnosis traditionally involves complex formula conversions to extract characteristics and categorize faults. However, such a method is unsuitable for hardware implementation due to high resource usage. This paper proposes a convolution neural network (CNN) approach for fault classification and hardware acceleration using ternary quantization and batch normalization techniques to reduce data access for weights and improve accuracy. The proposed CNN hardware accelerator is implemented on FPGA (VC707) and reduces memory usage by 83.8% compared to floating-point operations. Furthermore, the proposed method achieves 97.6% accuracy in CNC machinery fault classification.