Electronics and Communication Engineering, GMRIT, Rajam, Andhra Pradesh, India.
Research Article
World Journal of Advanced Research and Reviews, 2022, 16(01), 761-766
Received on 21 September 2022; revised on 25 October 2022; accepted on 27 October 2022
The convolutional neural network (CNN) is the most widely used machine learning technique within the fields of image and video processing. It is primarily used to categorize images using vast datasets. This require a lot of calculations. The effectiveness of a field-programmable gate array (FPGA) as a hardware accelerator for CNNs will give excellent performance at low power budgets. The employment of the Winograd algorithm can reduce the number of processing stages in CNN. 2-D convolution is employed for the bulk of calculations in CNNs. The tactic for computing convolution for smaller filter sizes that uses Winograd minimum filtering is the handiest. The comparison of computation complexity for multiplications will be performed using Matlab. The architecture of the Winograd-based processing element, RTL coding in Verilog HDL, and the test bench are designed to examine the performance. The Xilinx Vivado / Cadence tool will be used to implement the processing element in the convolution unit on an FPGA or ASIC.
Convolutional Neural Network (CNN); Filter; field-programmable gate array (FPGA); Winograd; Vivado; Matlab
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Vaddiraju Venkata Satya Sreya, Lanka Mohan Satish, Velaga Hemu Sai and Sasanapuri Sai Vijaya Krishna. Assessment of Winograd-based processor element for CNN processor and ASIC implementation of convolution processor element. World Journal of Advanced Research and Reviews, 2022, 16(1), 761-766. Article DOI: https://doi.org/10.30574/wjarr.2022.16.1.1101