Field-Programmable Gate Array-Based Ultra-Low Power Discrete Fourier Transforms for Closed-Loop Neural Sensing.

Area of Science:

• Biomedical Engineering
• Signal Processing
• Digital Systems

Background:

• Digital implementations of Discrete Fourier Transforms (DFT) are crucial for analyzing biopotentials and quantifying neurological disease biomarkers for adaptive deep brain stimulation.
• Fast Fourier Transform (FFT) algorithms consume significant power, posing challenges for battery-operated implantable medical devices in resource-constrained environments.

Purpose of the Study:

• To introduce an ultra-low power Fourier transform method suitable for resource-constrained embedded applications.
• To present a minimal architecture single-delay feedback discrete Fourier transform (mSDF-DFT) that prioritizes logic complexity reduction for energy efficiency.

Main Methods:

• Development of a novel minimal architecture single-delay feedback discrete Fourier transform (mSDF-DFT).
• Evaluation of the mSDF-DFT architecture for ultra-low power field programmable gate array applications.
• Comparison of energy and power consumption against state-of-the-art FFT methods.

Main Results:

• The mSDF-DFT achieved a 33% reduction in dynamic power compared to state-of-the-art FFT algorithms.
• Resource utilization was reduced by 4% in a neural sensing application using the mSDF-DFT.
• Demonstrated significant energy and power improvements in ultra-low power scenarios.

Conclusions:

• The mSDF-DFT presents a viable ultra-low power alternative to traditional FFT methods for biopotential analysis.
• The architecture is particularly beneficial for closed-loop deep brain stimulation and other implantable medical devices.
• The mSDF-DFT is easily extendable to various ultra-low power embedded applications beyond medical devices.