Work-in-Progress: Drama: A High Efficient Neural Network Accelerator on FPGA using Dynamic Reconfiguration

In this paper, we propose a high efficient neural network accelerator on FPGA by using dynamic reconfiguration, named Drama. Firstly, we design a high-efficient hardware architecture and provide a hardware template that can generate optimal configuration for each layer. Then, to explore the key features of the neural network models, we employ a layer-clustering algorithm to classify different layers. After that, we transform CNN models into task sequences. To accomplish the execution of the sequence, the FPGA-based hardware is able to switch the accelerator with dynamic reconfiguration and offload the related tasks to the accelerator at runtime. Preliminary results on the FPGA platform demonstrate that Drama is able to improve the performance significantly due to the dynamic reconfiguration techniques.