Optimization Strategies for Neural Network Deployment on FPGA

This research article presents optimization strategies for deploying deep neural networks (DNNs) on field-programmable gate arrays (FPGAs), focusing on energy-efficient real-time face detection as a use case. The paper outlines the challenges associated with DNN deployment on FPGA platforms, including limited computational resources and the need for real-time performance. It details various optimization methodologies, such as architectural modifications, fixed-point quantization, computation reordering, and parallelization techniques, aimed at enhancing the efficiency of DNN inference. The authors validate these strategies using the FaceBoxes model for face detection, demonstrating significant improvements in efficiency and energy consumption on the AMD Xilinx ZCU102 board. The study contributes to the field of FPGA-based DNN acceleration by providing a systematic approach to optimizing neural network inference for real-time applications. It also discusses future work that could extend these methodologies to other DNN architectures, emphasizing the broad applicability of the proposed techniques.