Fpga Implementation of Precise Convolutional Neural Network for Extreme Learning Machine
Sakthivel R1, Suburaaj R2

1Sakthivel R*, School of Electronics Engg., VIT, Vellore, India.
2Suburaaj R, School of Electronics Engg., VIT, Vellore, India.
Manuscript received on May 16, 2020. | Revised Manuscript received on May 16, 2020. | Manuscript published on June 10, 2020. | PP: 470-480 | Volume-9 Issue-8, June 2020. | Retrieval Number: H6501069820/2020©BEIESP | DOI: 10.35940/ijitee.H6501.069820
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Feed-forward neural networks can be trained based on a gradient-descent based backpropagation algorithm. But, these algorithms require more computation time. Extreme Learning Machines (ELM’s) are time-efficient, and they are less complicated than the conventional gradient-based algorithm. In previous years, an SRAM based convolutional neural network using a receptive – field Approach was proposed. This neural network was used as an encoder for the ELM algorithm and was implemented on FPGA. But, this neural network used an inaccurate 3-stage pipelined parallel adder. Hence, this neural network generates imprecise stimuli to the hidden layer neurons. This paper presents an implementation of precise convolutional neural network for encoding in the ELM algorithm based on the receptive – field approach at the hardware level. In the third stage of the pipelined parallel adder, instead of approximating the output by using one 2-input 15-bit adder, one 4-input 14-bit adder is used. Also, an additional weighted pixel array block is used. This weighted pixel array improves the accuracy of generating 128 weighted pixels. This neural network was simulated using ModelSim-Altera 10.1d and synthesized using Quartus II 13.0 sp1. This neural network is implemented on Cyclone V FPGA and used for pattern recognition applications. Although this design consumes slightly more hardware resources, this design is more accurate compared to previously existing encoders. 
Keywords: Convolutional Neural Network (CNN), Extreme Learning Machine (ELM), Field Programmable Gate Array (FPGA), Neuromorphic Computing, Pattern Recognition, Receptive-Field (RF), Very-Large Scale Integration (VLSI).
Scope of the Article: Machine Learning