Cellular Neural Networks for FPGAs with OpenCL

Cellular Neural Networks (CNNs) are an inherently parallel computational model for multiple applications, and they are especially appropriate for image processing tasks. Besides of implementing them with analogue electronic circuits, they can be simulated on digital processor architectures like CPUs and GPUs, with the drawback of limited parallelism. FPGAs offer a fine-grained parallel execution with low power consumption and are thus attractive for embedded systems like smart cameras, for which it is not possible to use a full-featured CPU or GPU with tens or hundrets of watts. The drawback of implementing CNNs with FPGAs, to profit from the high performance-to-power ratio, is the time-consuming design process with conventional hardware descriptions languages. High-level-synthesis, e.g., from OpenCL, eases the process of generating CNNs in FPGAs. By using the OpenCL programming model, the programmer can explicitly express the parallel nature of CNNs in a platform-independent way. To investigate its applicability to CNNs, we compare the execution of an unmodified OpenCL kernel on a recent CPU with an FPGA design generated with Altera's SDK for OpenCL. The results show, that though the CPU is faster, the FPGA solution performs better in terms of energy efficiency and fits for smart camera systems.