Kernel-Level Energy-Efficient Neural Architecture Search for Tabular Dataset
This addresses energy efficiency for tabular data applications, offering a domain-specific improvement over existing methods.
The paper tackled the problem of high energy consumption in neural networks by introducing an energy-efficient Neural Architecture Search (NAS) method that directly minimizes energy while maintaining accuracy for tabular datasets, achieving up to 92% reduction in energy consumption compared to conventional NAS.
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.