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.

我们提出了激活共享多分辨率（ASMR）坐标网络，通过多分辨率坐标分解和分层调制，实现了激活在数据网格之间的共享，从而实现了近似 O (1) 的推理复杂度，大大减少了硬件约束下的推理效率。实验证明，ASMR 能够将普通 SIREN 模型的 MAC 计数降低多达 500 倍，同时实现更高的重建质量。