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PatchINR reduces INR inference latency by 75% using patch-based approach

By PulseAugur Editorial · [2 sources] ·

Researchers have developed PatchINR, a novel patch-based approach to Implicit Neural Representations (INRs) that significantly reduces computational costs for high-resolution signal modeling. By processing non-overlapping patches as fundamental units, PatchINR predicts entire pixel patches in a single forward pass, drastically cutting down inference queries compared to traditional per-pixel methods. This approach achieves comparable reconstruction quality while reducing inference latency by 75% with minimal parameter overhead. Additionally, a hardware acceleration architecture for FPGAs has been proposed to further enhance the efficiency of PatchINR. AI

IMPACT This patch-based approach could enable more efficient and scalable applications of implicit neural representations in high-resolution signal modeling.

RANK_REASON The cluster describes a new research paper detailing a novel method and hardware architecture for improving the efficiency of Implicit Neural Representations.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

PatchINR reduces INR inference latency by 75% using patch-based approach

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The cluster describes a new research paper detailing a novel method and hardware architecture for improving the efficiency of Implicit Neural Representations.
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COVERAGE [2]

  1. arXiv cs.CV TIER_1 English(EN) · Jiachen Ren, Wenyong Zhou, Taiqiang Wu, Yuxin Cheng, Xincheng Feng, Zhengwu Liu, Ngai Wong ·

    PatchINR: Patch-Based Implicit Neural Representations for Efficient and Scalable Inference

    arXiv:2606.25534v1 Announce Type: new Abstract: 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 co…

  2. arXiv cs.CV TIER_1 English(EN) · Ngai Wong ·

    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…

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