Quantization for CNN Inference on FPGA
What Is Quantization? In signal processing, quantization is the process of mapping a continuous range of values to a discrete (integer) set. In deep learning and hardware acceleration, it specifically refers to: Converting floating-point (FP32, FP16, or BF16) model weights and activations to lower-bit integers (INT8, INT4, etc.) in order to reduce memory footprint and computational cost. Quantization is the bridge that makes neural networks practical on resource-constrained hardware. Why Quantization Is Essential for FPGA CNN Implementation FPGAs have a fixed amount of logic, DSP slices, and BRAM. Floating-point arithmetic is expensive in all three dimensions: ...