摘要
Recently, convolutional neural networks have achieved great success in high-resolution computational imaging applications such as super-resolution, image denoising, and image style transfer. However, it demands an enormous number of external memory access, i.e. DRAM bandwidth, and intensive computation while inferencing deeper models for high-quality images. In this letter, an energy-efficient computational imaging neural engine, CINE, is proposed with three key features: 1) overlapped stripe inference flow; 2) structure-sparse convolution kernel; 3) weight-rotated allocation unit. As a result, CINE can provide 4.6-8.3 TOP/W of energy efficiency for high-quality computational imaging applications.