ABM-SpConv: A Novel Approach to FPGA-Based Acceleration of Convolutional Neural Network Inference

Hardware accelerators for convolutional neural network (CNN) inference have been extensively studied in recent years. The reported designs tend to utilize a similar underlying architecture based on multiplier-accumulator (MAC) arrays, which has the practical consequence of limiting the FPGA-based accelerator performance by the number of available on-chip DSP blocks, while leaving other resource under-utilized. To address this problem, we consider a transformation to the convolution computation, which leads to transformation of the accelerator design space and relaxes the pressure on the required DSP resources. We demonstrate that our approach enables us to strike a judicious balance between utilization of the on-chip memory, logic, and DSP resources, due to which, our accelerator considerably outperforms state of the art. We report the effectiveness of our approach on a Stratix-V GXA7 FPGA, which shows 55% throughput improvement, while using 6.25% less DSP blocks, compared to the best reported CNN accelerator on the same device.