  We present a new efficient OpenCL-based Accelerator for large scale
Convolutional Neural Networks called Fast Inference on FPGAs for Convolution
Neural Network (FFCNN). FFCNN is based on a deeply pipelined OpenCL kernels
architecture. As pointed out before, high-level synthesis tools such as the
OpenCL framework can easily port codes originally designed for CPUs and GPUs to
FPGAs, but it is still difficult to make OpenCL codes run efficiently on FPGAs.
This work aims to propose an efficient FPGA implementation of OpenCL
High-Performance Computing Applications. To do so, a Data reuse and task
mapping techniques are also presented to improve design efficiency. In
addition, the following motivations were taken into account when developing
FFCNN: 1) FFCNN has been designed to be easily implemented on Intel OpenCL SDK
based FPGA design flow. 2) In FFFCN, different techniques have been integrated
to improve the memory band with and throughput. A performance analysis is
conducted on two deep CNN for Large-Scale Images classification. The obtained
results, and the comparison with other works designed to accelerate the same
types of architectures, show the efficiency and the competitiveness of the
proposed accelerator design by significantly improved performance and resource
utilization.
