Teng, K.-H., Raju, S., Zhu, D., Lim, J. L. K., Chen, D. S.-H., Ching, E. W. L., Jaibir, S., Joshua, L. E.-Y., Ng, E. J., Chuan, K. C. T., & Lal, A. (2021). An On-Chip 2-D DFT Accelerator Ultrasonic Wavefront for Convolutional Neural Networks. 2021 IEEE USNC-URSI Radio Science Meeting (Joint with AP-S Symposium). https://doi.org/10.23919/usnc-ursi51813.2021.9703556
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.
This research / project is supported by the A*STAR - AME Programmatic Fund - Ultrasonic Wavefront Computing
Grant Reference no. : A19E8b0102