⚡ 本页包含 AI 生成的分析内容，仅供参考
该论文提出了一款面向移动应用的超分辨率处理器，通过利用伪FP6稀疏性实现了0.52mJ/帧的超低能耗和107fps的高帧率。解决了移动设备上SRCNN推理时的高延迟和高功耗问题。
increasingly employed across various domains. The ability to recover fine details is especially critical in mobile applications such as gaming, video, and photography [1]. However, mobile devices are usually sensitive to latency and power consumption, so SR processors that drain a lot of battery are not practical. Designing energy-efficient hardware for convolutional neural network (CNN)-based SR (SRCNN) [2] to meet these constraints poses three key challenges, as shown in Fig. 2.10.1. Firstly, prior works [3, 4] have introduced substantial on-chip memory for multi-dimentional (multi-dim) overlapping regions (OR) in layer-fusion dataflow to achieve low external bandwidth. However, to further improve energy efficiency, multi-dim OR and external memory access should both be reduced. Secondly, novel lowbit-width but high-dynamic-range computation i
Xuyang Duan, Xinhua Shi, Zikang Zhou, Zhiyi Shu, Yitong Rong, Yufan Chen,
Zhen Yang, Menghan Li, Jun Han Fudan University, Shanghai, China Super-resolution (SR) is essential for enhancing digital image quality and is