Approximate Fast Fourier Transform-based preprocessing for Edge AI

The emerging Edge Artificial Intelligence (AI) paradigm is a key driver of innovation in many areas such as audio or industrial sensor data processing. Edge AI relies on the integration of machine and deep learning-based signal processing into even the smallest embedded devices thus enabling local intelligence in real-world applications. Nevertheless, classical signal processing techniques such as the Fast Fourier Transform (FFT) still remain an essential component of Edge AI systems, especially in the context of data preprocessing. However, their optimization with respect to resource requirements is often neglected although it can increase the performance of the overall system. In this paper, we present a new approximate FFT (AFFT) approach that enables the end-to-end resource optimization of digital signal processing (DSP) pipelines in Edge AI systems. The approach combines an aggressive FFT parameter quantization with error mitigation techniques to trade off accuracy and performance. To evaluate the methodology, a flexible code generation framework is implemented. We analyze the accuracy of the AFFT, demonstrate its capabilities in an audio classification use-case involving a convolutional neural network (CNN) and benchmark the approach on an ARM Cortex-M4-based System-on-Chip (SoC). Our methodology maintains the classification accuracy of the CNN close to the full-precision level while outperforming the ARM CMSIS FFT both in execution time and energy consumption.