⚡ 本页包含 AI 生成的分析内容，仅供参考
提出VISTA处理器，用于加速视频和图像的空间/时间插值CNN。该处理器功耗704mW，支持4K-UHD分辨率，解决了高吞吐视频CNN推理的三个设计挑战。
Video convolutional neural networks (CNNs) have achieved great success in highresolution imaging applications, such as video super-resolution (VSR) and demonstrated superior quality and temporal consistency by leveraging time information. In particular, as shown in Fig. 2.6.1, video CNNs can also support applications like video-frame interpolation (VFI) which is difficult to achieve by single-image CNNs. Therefore, video CNNs have enormous potential for next-generation imaging/display technology. However, there are three design challenges while inferencing high-throughput video CNNs. Firstly, massive external memory access (EMA) and computation complexity are induced since they both grow accordingly as the number of input frames (N) increases. Secondly, tremendous memory usage of feature maps (FMs) is required for supporting cross-frame alignment with in-order frame scheduling. Thirdly, supporting deformable convolution
Kai-Ping Lin, Jia-Han Liu, Jyun-Yi Wu, Hong-Chuan Liao, Chao-Tsung Huang
National Tsing Hua University, Hsinchu, Taiwan