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An On-Chip 2-D DFT Accelerator Ultrasonic Wavefront for Convolutional Neural Networks

A 2-dimensional discrete Fourier transform (2-D DFT) is a required preprocessing step for convolutional neural networks (CNNs) to perform matrix multiplication in convolutional layers. Here we present an ultrasonic wavefront-based architecture for CNNs that harness the wave propagation diffraction physic to perform Fourier transform (FT) effectively. The computation is improved to O(N) compare to Fast Fourier transform (FFT) with O(N(log2N). In addition, analysis of the proposed ultrasonic wavefront scheme is described in this paper.