FPGA Based Cellular Neural Network Optimization: From Design Space to System

Cellular Neural Network (CeNN) is considered as a powerful paradigm for embedded devices. Its analog and mix-signal hardware implementations are proved to be applicable to high-speed image processing, video analysis and medical signal processing with its efficiency and popularity limited by smaller implementation size and lower precision. Recently, various digital implementations of CeNNs on FPGA have attracted researchers from both academia and industry due to its high flexibility and short time-to-market. However, most existing implementations are typically bounded utilizing the advantages of FPGA platform inadequately with unnecessary design and computational redundancy that prevents speedup. To address these issues, we propose a multi-level optimization framework for energy efficient CeNN implementations on FPGAs. In particular, the optimization framework is featured with three level optimizations: system-, module-, and design-space-level, with focus on computational redundancy and attainable performance, respectively. Experimental results show that with various configurations our framework can achieve an energy efficiency improvement of 3.54x and up to 3.88x speedup compared with existing implementations.