Exploring a Layer-based Pre-implemented Flow for Mapping CNN on FPGA

Convolutional Neural Networks are compute-intensive learning models that have demonstrated ability and effectiveness in solving complex learning problems. However, developing a high-performance FPGA accelerator for CNN often demands high programming skills, hardware verification, precise distribution localization, and long development cycles. Besides, CNN depth increases by reuse and replication of multiple layers. This paper proposes a programming flow for CNN on FPGA to generate high-performance accelerators by assembling CNN pre-implemented components as a puzzle based on the graph topology. Using pre-implemented components allows us to use the minimum of resources necessary, predict the performance, and gain in productivity since there is no need to synthesize any HDL code. Furthermore, components can be reused for a different range of applications. Through prototyping, we demonstrated the viability and relevance of our approach. Experiments show a productivity improvement of up to 69% compared to a traditional FPGA implementation while achieving over 1.75x higher Fmax with lower resources and power consumption.