mertz1999/CNN_ON_FPGA
implement convolution neural network on FPGA based on VHDL design
This project provides a ready-made design for implementing a Convolutional Neural Network (CNN) directly onto an FPGA using VHDL code. It takes a pre-trained CNN model (like one trained in PyTorch) and converts its parameters into a fixed-point format suitable for hardware, outputting VHDL files that define the CNN's full architecture on the FPGA. This is for electronics engineers or hardware designers who need to integrate AI inference capabilities into specialized hardware without using high-level synthesis tools.
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Use this if you need fine-grained control over the hardware implementation of a CNN for a specific embedded system or custom chip design, especially for low-latency or low-power applications.
Not ideal if you are a software developer looking for a Python library to accelerate CNNs, or if you prefer using high-level synthesis tools for FPGA development.
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Oct 08, 2021
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