Research on Approximate Computation of Signal Processing Algorithms for AIoT Processors Based on Deep Learning

In the post-Moore era, the excessive amount of information brings great challenges to the performance of computing systems. To cope with these challenges, approximate computation has developed rapidly, which enhances the system performance with minor degradation in accuracy. In this paper, we investigate the utilization of an Artificial Intelligence of Things (AIoT) processor for approximate computing. Firstly, we employed neural architecture search (NAS) to acquire the neural network structure for approximate computation, which approximates the functions of FFT, DCT, FIR, and IIR. Subsequently, based on this structure, we quantized and trained a neural network implemented on the AI accelerator of the MAX78000 development board. To evaluate the performance, we implemented the same functions using the CMSIS-DSP library. The results demonstrate that the computational efficiency of the approximate computation on the AI accelerator is significantly higher compared to traditional DSP implementations. Therefore, the approximate computation based on AIoT devices can be effectively utilized in real-time applications.