  Coordinate network or implicit neural representation (INR) is a fast-emerging
method for encoding natural signals (such as images and videos) with the
benefits of a compact neural representation. While numerous methods have been
proposed to increase the encoding capabilities of an INR, an often overlooked
aspect is the inference efficiency, usually measured in multiply-accumulate
(MAC) count. This is particularly critical in use cases where inference
throughput is greatly limited by hardware constraints. To this end, we propose
the Activation-Sharing Multi-Resolution (ASMR) coordinate network that combines
multi-resolution coordinate decomposition with hierarchical modulations.
Specifically, an ASMR model enables the sharing of activations across grids of
the data. This largely decouples its inference cost from its depth which is
directly correlated to its reconstruction capability, and renders a near O(1)
inference complexity irrespective of the number of layers. Experiments show
that ASMR can reduce the MAC of a vanilla SIREN model by up to 500x while
achieving an even higher reconstruction quality than its SIREN baseline.
