Gradient-based Automatic Per-Weight Mixed Precision Quantization for Efficient Neural Network Inference on FPGAs

This work presents High Granularity Quantization (HGQ), a novel quantization-aware training method that automatically optimizes the bitwidth of each weight and activation in a neural network to minimize on-chip resource usage while preserving accuracy for FPGA deployment.