Abstract
Many studies estimate energy consumption using proxy metrics like memory usage, FLOPs, and inference latency, with the assumption that reducing these metrics will also lower energy consumption in neural networks. This paper, however, takes a different approach by introducing an energy-efficient Neural Architecture Search (NAS) method that directly focuses on identifying architectures that minimize energy consumption while maintaining acceptable accuracy. Unlike previous methods that primarily target vision and language tasks, the approach proposed here specifically addresses tabular datasets. Remarkably, the optimal architecture suggested by this method can reduce energy consumption by up to 92% compared to architectures recommended by conventional NAS.
Abstract (translated)
许多研究通过使用代理指标（如内存使用量、FLOPs 和推理延迟）来估算能源消耗，假设减少这些指标也会降低神经网络的能耗。然而，本文采用了一种不同的方法，引入了一种节能型神经架构搜索（NAS）方法，该方法直接专注于识别在保持可接受精度的同时最小化能耗的架构。与以往主要针对视觉和语言任务的方法不同，这里提出的方法特别针对表格数据集。值得注意的是，此方法建议的最佳架构相比传统 NAS 推荐的架构可以减少高达 92% 的能源消耗。
URL
https://arxiv.org/abs/2504.08359
