Hardware Implementation Convolutional Neural Network is a M.Tech project topic for Electrical Engineering. It gives students a clear starting point for research, implementation planning, and documentation.
Hardware Implementation Convolutional Neural Network Project Details
| Abstract |
This research-oriented project focuses on the hardware implementation of a convolutional neural network (CNN) tailored for real-time image recognition systems on Field Programmable Gate Array (FPGA) platforms. Utilizing a model-driven design methodology, the architecture transitions from high-level algorithmic representations to synthesizable hardware description language (HDL) code. The baseline neural network architecture consists of seven layers, achieving a high-fidelity classification accuracy of 99.54% during initial software-level validation. To address the stringent resource constraints inherent to edge-computing hardware, optimization strategies are systematically explored, including the reduction of filter counts and the modification of kernel dimensions. This structural optimization results in a trade-off, adjusting the prediction accuracy to 86.96% while significantly lowering hardware
resource utilization. Logical synthesis of the generated HDL code is executed to evaluate hardware parameters such as logic utilization, power consumption, and maximum operating frequency. The simulation results validate the theoretical assumptions, demonstrating the viability of the optimized CNN architecture for deployment in resource-constrained embedded systems, autonomous driving, robotics, and real-time quality control applications.
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| Reference Paper |
Hardware Implementation of a Convolutional Neural Network |
| Domain |
Electrical Engineering |
| Sub-Domain |
Control Systems / Advanced Control / Neural Network Control |
| PDF Download |
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| Get Help |
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