LorenzoValente3/Autoencoder-for-FPGA
Autoencoder model for FPGA implementation using hls4ml. Repository for Applied Electronics Project.
This project helps embedded systems engineers and researchers deploy deep learning models onto Field-Programmable Gate Arrays (FPGAs). It takes a trained autoencoder model, specifically for machine vision tasks like image reconstruction, and converts it into a highly optimized FPGA implementation. The output is a compact and efficient hardware design ready for deployment, enabling faster processing and lower power consumption for tasks like real-time image analysis.
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Use this if you need to run machine learning models directly on FPGA hardware for embedded vision systems, and require significant optimizations in model size, speed, and energy use.
Not ideal if you are a software developer deploying models on GPUs or CPUs, or if your application does not require the low-latency and efficiency benefits of FPGA hardware.
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Mar 21, 2023
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