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New framework predicts CNN inference costs on GPUs with high accuracy

By PulseAugur Editorial · [2 sources] ·

Researchers have developed CARB, a new framework designed to accurately predict the inference cost of Convolutional Neural Networks (CNNs) on resource-constrained GPU platforms. Through a detailed characterization of over 13,000 CNN configurations on RTX 5090 and RTX 3080 GPUs, the study revealed distinct scaling behaviors for energy, latency, and memory usage. CARB utilizes these findings to jointly predict these costs with high accuracy (R2 ~0.99) and implements a screening workflow that rapidly narrows down large design spaces to a prioritized shortlist. AI

IMPACT Enables more efficient deployment of CNNs on edge devices by accurately predicting energy, latency, and memory costs.

RANK_REASON The cluster describes a research paper detailing a new framework for predicting CNN inference costs on GPUs.

Read on Hugging Face Daily Papers →

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

New framework predicts CNN inference costs on GPUs with high accuracy

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The cluster describes a research paper detailing a new framework for predicting CNN inference costs on GPUs.
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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Linh Nguyen, Zhixin Pan ·

    CARB: A Characterization-Guided Framework for CNN Inference Cost Prediction and Deployment Screening

    arXiv:2608.10506v1 Announce Type: cross Abstract: Accurate pre-deployment estimation of CNN inference cost--energy, latency, and peak memory--is increasingly critical as models are deployed on resource-constrained GPU platforms. Existing approaches rely on FLOPs, latency measurem…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    CARB: A Characterization-Guided Framework for CNN Inference Cost Prediction and Deployment Screening

    Accurate pre-deployment estimation of CNN inference cost--energy, latency, and peak memory--is increasingly critical as models are deployed on resource-constrained GPU platforms. Existing approaches rely on FLOPs, latency measurements, or single-device profiling as energy proxies…

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