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Real-time image processing is a key area of focus, but computationally intensive. Neural networks effectively address classification tasks, but they are not always a viable option, particularly in environments where high power consumption or computational requirements are limiting factors. Hardware devices such as Field-Programmable Gate Arrays (FPGAs) offer significant parallelization capabilities that can be fully exploited when the implemented circuit is composed solely of logic gates. In addition, FPGAs are also interesting alternatives to traditional GPU-based implementations in terms of power consumption and reconfiguration capabilities. They can be used as a demonstration platform to validate a hardware design that can be later manufactured, creating the final Application-Specific Integrated Circuit (ASIC). This paper introduces a practical demonstration platform based on an FPGA that highlights the great capabilities of logic neural networks, a type of neural network constructed exclusively with logic gates.
By harnessing FPGA parallelization and logic gates, we have achieved a balance between computational power and real-time performance. This approach ensures that image classification occurs at speeds on the order of nanoseconds. This ultra-fast processing is well-suited for real-time image analysis applications across various domains. Industries that rely on quality control, such as manufacturing, will benefit from rapid and precise assessments. In the field of medical image processing, where quick diagnoses are crucial, this technology promises transformative advancements. The demonstration platform developed serves as a proof of concept for logic neural networks, offering a solution to the challenge of real-time image processing and representing the first step towards the implementation of future architectures of logic networks in hardware.
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