An FPGA-based Solution for Convolution Operation Acceleration

• URL: http://arxiv.org/abs/2206.04520v1
• Date: Thu, 9 Jun 2022 14:12:30 GMT
• Title: An FPGA-based Solution for Convolution Operation Acceleration
• Authors: Trung Dinh Pham, Bao Gia Bach, Lam Trinh Luu, Minh Dinh Nguyen, Hai Duc Pham, Khoa Bui Anh, Xuan Quang Nguyen, Cuong Pham Quoc
• Abstract summary: This paper proposes an FPGA-based architecture to accelerate the convolution operation. The project's purpose is to produce an FPGA IP core that can process a convolutional layer at a time.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Hardware-based acceleration is an extensive attempt to facilitate many computationally-intensive mathematics operations. This paper proposes an FPGA-based architecture to accelerate the convolution operation - a complex and expensive computing step that appears in many Convolutional Neural Network models. We target the design to the standard convolution operation, intending to launch the product as an edge-AI solution. The project's purpose is to produce an FPGA IP core that can process a convolutional layer at a time. System developers can deploy the IP core with various FPGA families by using Verilog HDL as the primary design language for the architecture. The experimental results show that our single computing core synthesized on a simple edge computing FPGA board can offer 0.224 GOPS. When the board is fully utilized, 4.48 GOPS can be achieved.

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