A High-Throughput Reconfigurable Processing Array for Neural Networks

FPGA-based neural-networks typically leave performance on the table because the DSP resources run at less than a third of the peak clock rate. This paper presents a processing array architected to consistently achieve timing closure at 100% of the peak DSP clock rate with standard FPGA tools. In the HDL design environment, our processing array operates at the peak DSP clock rates on Xilinx UltraScale (741 MHz) and UltraScale+ (891 MHz) devices. To enhance portability and consistency of timing closure, this array operates at a high clock rate while data SRAMs run at a fraction of this rate. As a proof of concept, this paper outlines a processing array for matrix multiplication and convolution, the most compute-intensive operations of a convolutional neural network (CNN).