A low power and real-time hardware recurrent neural network for time series analysis on wearable devices

The research presented in this paper addresses the exploitation of Deep Learning methods on wearable devices. We propose a hardware architecture capable of analyzing time series signals through a Recurrent Neural Network implemented on FPGA technology. This architecture has been validated using a real dataset, which includes three-axial accelerometer data acquired by a wearable device used for fall detection. The experiments have been conducted considering different devices and demonstrates that the proposed hardware architecture outperforms the state of the art solutions both in terms of processing time and power consumption. Indeed, the proposed architecture is real-time compliant in the elaboration of the fall detection dataset adopted for the validation. The power consumption is in the order of dozens μW. Finally, futher functionalities could be added in the same chip since the resource usage is low.