研究人员开发了CARB，一个旨在部署前准确预测卷积神经网络（CNN）推理成本的框架。通过对不同GPU上CNN配置进行广泛的表征，他们发现能耗、延迟和内存使用量具有不同的缩放规律。CARB利用这些发现，通过一个能够快速筛选潜在CNN候选者的工作流程，联合高精度地预测这些指标，将大的设计空间缩减为可管理的候选名单。 AI
影响 通过准确预测能耗、延迟和内存使用量，能够更有效地在资源受限的硬件上部署CNN。
排序理由 该条目描述了一篇关于CNN推理成本预测框架的新研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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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…
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…