HGQ-LUT delivers a practical LUT-aware training framework with new tensor-based layers, heterogeneous quantization, and a resource surrogate that automates accuracy-efficiency trade-offs for FPGA DNN inference.
Cluster counting algorithm for the cepc drift chamber using lstm and dgcnn
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HGQ-LUT: Fast LUT-Aware Training and Efficient Architectures for DNN Inference
HGQ-LUT delivers a practical LUT-aware training framework with new tensor-based layers, heterogeneous quantization, and a resource surrogate that automates accuracy-efficiency trade-offs for FPGA DNN inference.