Advanced Quantization Schemes to Increase Accuracy, Reduce Area, and Lower Power Consumption in FFT Architectures.

Abstract

This paper explores new advanced quantization schemes for fast Fourier transform (FFT) architectures. In previous works, FFT quantization has been treated theoretically or with the sole aim of improving accuracy. In this work, we go one step beyond by considering also the implications that quantization schemes have on the area and power consumption of the architecture. To achieve this, we have analyzed the mathematical operations carried out in FFT architectures and explored the changes that benefit all the figures of merit. By combining or alternating truncation and rounding, and using the half-unit biased (HUB) representation in the different computations of the architecture, we have achieved quantization schemes that increase accuracy, reduce area, and lower power consumption simultaneously. This win-win result improves multiple figures of merit without worsening any other, making it a valuable strategy to optimize FFT architectures.