Energy-efficient digital design of reliable, low-throughput wireless biomedical systems

EEG;Digital design;Subthreshold;Low power;Data compression;Energy-efficiency;Wireless systems

Tolbert, Jeremy Reynard ; Electrical and Computer Engineering

University:Georgia Institute of Technology

Department:Electrical and Computer Engineering

关键词: EEG;    Digital design;    Subthreshold;    Low power;    Data compression;    Energy-efficiency;    Wireless systems;   

Others  :  https://smartech.gatech.edu/bitstream/1853/45787/1/tolbert_jeremy_r_201212_phd.pdf

美国|英语

来源: SMARTech Repository

【 摘 要 】

The main objective of this research is to improve the energy efficiency of low throughput wireless biomedical systems by employing digital design techniques.The power consumed in conventional wireless EEG (biomedical) systems is dominated by digital microcontroller and the radio frequency (RF) transceiver.To reduce the power associated with the digital processor, data compression can reduce the volume of data transmitted.An adaptive data compression algorithm has been proposed to ensure accurate representations of critical epileptic signals, while also preserving the overall power.Further advances in power reduction are also presented by designing a custom baseband processor for data compression.A functional system has been hardware verified and ASIC optimized to reduce the power by over 9X compared to existing methods.The optimized processor can operate at 32MHz with a near threshold supply of 0.5V in a conventional 45nm technology.While attempting to reach high frequencies in the near threshold regime, the probability of timing violations can reduce the robustness of the system.To further optimize the implementation, a low voltage clock tree design has been investigated to improve the reliability of the digital processor.By implementing the proposed clock tree design methodology, the digital processor can improve its robustness (by reducing the probability of timing violations) while reducing the overall power by more than 5 percent.Future work suggests examining new architectures for low-throughput processing and investigating the proposed systems' potential for a multi-channel EEG implementation.