Trainable Fixed-Point Quantization for Deep Learning Acceleration on FPGAs

Quantization is a crucial technique for deploying deep learning models on resource-constrained devices, such as embedded FPGAs. Prior efforts mostly focus on quantizing matrix multiplications, leaving other layers like BatchNorm or shortcuts in floating-point form, even though fixed-point arithmetic is more efficient on FPGAs. A common practice is to fine-tune a pre-trained model to fixed-point for FPGA deployment, but potentially degrading accuracy.
This work presents QFX, a novel trainable fixed-point quantization approach that automatically learns the binary-point position during model …

cs.cv cs.lg deep learning devices embedded fixed-point focus form fpgas matrix practice prior quantization