WenqiJiang/FPGA-Based-RNN-Accelerator-Using-Vivado-HLS

This project is a legacy educational or personal experiment (7+ years old) focused on mapping RNN logic to FPGA gates via HLS. With only 25 stars and zero recent activity, it serves as a historical reference rather than a competitive tool. From a competitive standpoint, it lacks any moat; the industry has moved beyond vanilla RNNs toward Transformers and LSTMs, and modern hardware acceleration for these architectures is now dominated by enterprise-grade stacks like Xilinx Vitis AI, hls4ml (for high-energy physics/low-latency), and Apache TVM. The defensibility is minimal as the logic is a standard C++ implementation of RNN math tailored for HLS pragmas. Furthermore, frontier labs and hardware giants have already internalized or superseded these capabilities with specialized NPU cores (like Apple's Neural Engine or Google's TPU) that offer significantly higher performance-per-watt than a generic HLS-derived RNN implementation.