With the rapid iteration of neural network algorithms, higher requirements were placed on the computational performance and memory access bandwidth of neural network accelerators. Simply increasing bandwidth cannot improve energy efficiency, so improving the data reuse rate is a hot research topic. From the perspective of supporting data reuse, a reconfigurable convolutional neural network ( CNN) accelerator based on elastic storage ( RCAES) was designed in this paper. Supporting elastic memory access and flexible data flow reduces data movement between the processor and memory, eases the bandwidth pressure and enhances CNN acceleration performance. The experimental results indicate that by conducting 1 × 1 convolution and 3 × 3 convolution when performing convolution calculations, the execution speed increased by 25.00% and 61.61% , respectively. The 3 × 3 maximum pooling speed was increased by 76.04% .