PatchINR: Patch-Based Implicit Neural Representations for Efficient and Scalable Inference
Implicit Neural Representation (INR) provides an effective approach for continuous signal modeling, but classical per-pixel inference results in quadratic growth in inference count, leading to dramatically increased computational costs in high-resolution application scenarios. To address this issue, we propose a patch-based approach that treats non-overlapping patches as fundamental processing units and predicts entire pixel patches in a single forward pass, significantly reducing the number of inference queries required. To validate the effectiveness of our approach, we propose a hardware acceleration architecture on the Field Programmable Gate Array (FPGA) platform for the INR model, wh...
| Authors | Jiachen Ren, Wenyong Zhou, Taiqiang Wu et al. |
|---|---|
| Primary category | cs.CV |
| Published | 24 June 2026 |
| Last updated at source | 24 June 2026 |
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