  In recent years, image and video delivery systems have begun integrating deep
learning super-resolution (SR) approaches, leveraging their unprecedented
visual enhancement capabilities while reducing reliance on networking
conditions. Nevertheless, deploying these solutions on mobile devices still
remains an active challenge as SR models are excessively demanding with respect
to workload and memory footprint. Despite recent progress on on-device SR
frameworks, existing systems either penalize visual quality, lead to excessive
energy consumption or make inefficient use of the available resources. This
work presents NAWQ-SR, a novel framework for the efficient on-device execution
of SR models. Through a novel hybrid-precision quantization technique and a
runtime neural image codec, NAWQ-SR exploits the multi-precision capabilities
of modern mobile NPUs in order to minimize latency, while meeting
user-specified quality constraints. Moreover, NAWQ-SR selectively adapts the
arithmetic precision at run time to equip the SR DNN's layers with wider
representational power, improving visual quality beyond what was previously
possible on NPUs. Altogether, NAWQ-SR achieves an average speedup of 7.9x, 3x
and 1.91x over the state-of-the-art on-device SR systems that use heterogeneous
processors (MobiSR), CPU (SplitSR) and NPU (XLSR), respectively. Furthermore,
NAWQ-SR delivers an average of 3.2x speedup and 0.39 dB higher PSNR over
status-quo INT8 NPU designs, but most importantly mitigates the negative
effects of quantization on visual quality, setting a new state-of-the-art in
the attainable quality of NPU-based SR.
