IEEE Circuits and Systems Magazine - Q4 2021 - 24
Feature
Precision Adaptive MFCC
Based on R2SDF-FFT and
Approximate Computing
for Low-Power Speech
Keywords Recognition
Bo Liu, Member, IEEE, Xiaoling Ding, Hao Cai, Member, IEEE, Wentao Zhu, Zhen Wang,
Weiqiang Liu, Senior Member, IEEE, and Jun Yang, Member, IEEE
Abstract
This paper presents system-architecture-circuits co-designs for
computing the MFCC feature extraction for speech keywords recognition.
The trade-off between accuracy and power consumption
under various background noises is achieved by using the
8-stage radix-2 single-path delay feedback FFT (R2SDF-FFT)
and the precision self-adaptive architecture with approximate
Digital Object Identifier 10.1109/MCAS.2021.3118175
Date of current version: 12 November 2021
24
IEEE CIRCUITS AND SYSTEMS MAGAZINE
computing. The R2SDF-FFT structure with the fine-grained bitwidth
quantization can reduce 35.7% of memory size. Approximate
multiplication and addition with Dual-Vdd are proposed to
further improve the FFT computing energy efficiency. Finally,
we present the precision self-adaptive MFCC architecture with
the proposed FFT, which can be dynamically configured to use
two calculation modes with different hardware settings according
to the input speech background noise. Implemented and
evaluated under 22 nm technology, the power consumption of
the proposed design can be reduced by up to 76.3%, while the
accuracy increased by 0.8%.
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IEEE Circuits and Systems Magazine - Q4 2021
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