An Efficient Implementation Approach to FFT Processor for Spectral Analysis

• Published in: IEEE transactions on instrumentation and measurement Vol. 72; pp. 1 - 13
• Main Authors: Hazarika, Jinti, Khan, Mohd. Tasleem, Ahamed, Shaik Rafi, Nemade, Harshal B.
• Format: Journal Article
• Language: English
• Published: New York IEEE 2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adders; Binary Multiplier; Clocks; Complexity theory; Electroencephalography; Fast Fourier transform (FFT); Fast Fourier transformations; Field programmable gate arrays; field-programmable gate array (FPGA); Fourier transforms; Hardware; Logic gates; low complexity; Machine learning; Microprocessors; Optimization; partial product generator (PPG); Power management; Spectral analysis; Spectrum analysis
• ISSN: 0018-9456; 1557-9662
• DOI: 10.1109/TIM.2023.3301891

This article presents an efficient hardware implementation approach to a variable-size fast Fourier transform (FFT) processor for spectral analysis. Due to its capability to handle different frame sizes, it can be adapted in situations where operating parameters necessitate adhering to different standard requirements. A serial real-valued processor with a new data-flow graph is considered, as it requires the least number of multipliers. By joint use of stage-specific optimization and multiplierless structure, the overall hardware efficiency of the proposed design is enhanced. Clock gating is employed to enable the variable-size processor operation along with power reduction. A fixed-point (FP) analysis of the proposed design is considered. The proposed novel multiplierless structure is based on shift and accumulation (SA). This also includes the generation (and sharing) of partial products (PPs) based on their symmetries. The proposed design offers low area and low power as compared with the state of the art. It is demonstrated for spectral analysis of electroencephalogram (EEG) signals for machine-learning-based epileptic seizure prediction on a field-programmable gate array (FPGA) platform.