Energy-Efficient Configurable Discrete Wavelet Transform for Neural Sensing Applications

Abstract

Highly integrated neural sensing microsystems are crucial to capture accurate signals for brain function investigations. In this paper, an energy-efficient configurable lifting-based discrete wavelet transform (DWT) is proposed for a high-density neural sensing microsystems to extract the features of neural signals by filtering the signals into different frequency bands. Based on the lifting-based DWT algorithm, the area and power consumption can be reduced by decreasing the computation circuits. Additionally, both the time window and mother wavelets can be adjusted via the configurable datapth. Moreover, the power-gating and clock-gating techniques are utilized to further reduce the energy consumption for the energy-limited bio-systems. The proposed configurable DWT is designed and implemented using TSMC 65nm CMOS low power process with total area of 0.11 mm(2) and power consumption of 26 mu W. Moreover, this proposed DWT is also implemented in Lattice MachXO2-1200 FPGA and integrated in a 2.5D heterogeneously integrated high-density neural-sensing microsystem with the power consumption of 211.2 mu W.