FlexAcc: Accelerating Batch Normalization through GPU-FPGA Integration

Convolutional neural networks are fundamental to deep learning, especially in computer vision. However, their computational demands, particularly during batch normalization, create significant inefficiencies due to excessive data movement between memory and processing units. To address this, we propose FlexAcc, a novel architecture that integrates GPUs and FPGAs to offload BN computations to FPGAs, reducing data movement and improving hardware utilization. FlexAcc accelerates end-to-end training performance by up to 1.1× across various models. This approach bridges the performance gap between convolutional and non-convolutional layers, advancing deep learning model deployment.