Greedy Prefetch for Reducing Off-Chip Memory Accesses in Convolutional Neural Network Inference

The high parameter and memory access demands of CNNs highlight the need to reduce off-chip memory accesses. While recent approaches have improved data reuse to lessen these accesses, simple and efficient prefetching methods are still lacking. This paper introduces a greedy prefetch method that uses data repetition to optimize the prefetching route, thus decreasing off-chip memory accesses. The method is also implemented in a hardware simulator to organize an deployment strategy with additional optimizations. Our deployment strategy outperforms recent works, with a maximum data reuse improvement of 1.98×.