With the rise of contactless communication and streaming services, Super-resolution (SR) in mobile devices has become one of the most important image processing technologies. Also, the popularity of high-end Application Processor (AP) and high-resolution display in mobile drives the development of the lightweight mobile SR-CNNs, which show the high reconstruction quality. However, the large size and wide dynamic range of both images and intermediate feature maps in CNN hidden layers pose challenges for mobile platforms. Constraints from the limited power and shared bandwidth on mobile platform, a low power and energy-efficient architecture is required.
This paper presents an image processing SoC exploiting non-sparse SR task. It contributes 2 following key features: 1) Heterogeneous architecture with only 8bit FP-FXP hybrid-precision for SR task, and 2) data lifetime-aware two-way optimized cache subsystem for energy-efficient depth-first image processing. With highly optimized heterogeneous cores and cache subsystem, our SoC presents 2.6x higher energy-efficiency than previous SRNPU and 107 frame-per-second (fps) framerate running 4x SR image generation to Full-HD scale with 0.92 mJ/frame energy consumption.
Related papers:
Z. Li, S. Kim, D. Im, D. Han and H. -J. Yoo, “An 0.92 mJ/frame High-quality FHD Super-resolution Mobile Accelerator SoC with Hybrid-precision and Energy-efficient Cache,” 2022 IEEE Custom Integrated Circuits Conference (CICC), 2022.
