  Conventional super-resolution (SR) schemes make heavy use of convolutional
neural networks (CNNs), which involve intensive multiply-accumulate (MAC)
operations, and require specialized hardware such as graphics processing units.
This contradicts the regime of edge AI that often runs on devices strained by
power, computing, and storage resources. Such a challenge has motivated a
series of lookup table (LUT)-based SR schemes that employ simple LUT readout
and largely elude CNN computation. Nonetheless, the multi-megabyte LUTs in
existing methods still prohibit on-chip storage and necessitate off-chip memory
transport. This work tackles this storage hurdle and innovates hundred-kilobyte
LUT (HKLUT) models amenable to on-chip cache. Utilizing an asymmetric
two-branch multistage network coupled with a suite of specialized kernel
patterns, HKLUT demonstrates an uncompromising performance and superior
hardware efficiency over existing LUT schemes. Our implementation is publicly
available at: https://github.com/jasonli0707/hklut.
