Design of deep convolutional neural network accelerator based on low-cost FPGA
Existing DCNN generate a large amount of inter-layer feature data during inference. To maintain real-time processing on embedded systems,a significant amount of on-chip storage is required to cache inter-layer feature maps. This paper proposes an inter-layer feature compression technique to significantly reduce off-chip memory access bandwidth. Additionally,a generic convolution computation scheme tailored for BRAM in FPGA is proposed,with optimizations made at the circuit level to reduce memory accesses and improve DSP computational efficiency,thereby greatly enhancing computation speed. Compared to running MobileNetV2 on a CPU,the proposed DCNN accelerator in this paper achieves a performance improvement of 6.3 times;compared to other DCNN accelerators of the same type,the proposed DCNN accelerator in this paper achieves DSP performance efficiency improvements of 17％ and 156％,respectively.
deep convolutional neural networkfield programmable gate arraydeep learning
万方数据
维普
