Hardware-aware Neural Architecture Search (HW-NAS) is a technique used to automatically design the architecture of a neural network for a specific task and target hardware. However, evaluating the performance of candidate architectures is a key challenge in HW-NAS, as it requires significant computational resources. To address this challenge, we propose an efficient hardware-aware evolution-based NAS approach called HW-EvRSNAS. Our approach re-frames the neural architecture search problem as finding an architecture with performance similar to that of a reference model for a target hardware, while adhering to a cost constraint for that hardware. This is achieved through a representation similarity metric known as Representation Mutual Information (RMI) employed as a proxy performance evaluator. It measures the mutual information between the hidden layer representations of a reference model and those of sampled architectures using a single training batch. We also use a penalty term that penalizes the search process in proportion to how far an architecture's hardware cost is from the desired hardware cost threshold. This resulted in a significantly reduced search time compared to the literature that reached up to 8000x speedups resulting in lower CO2 emissions. The proposed approach is evaluated on two different search spaces while using lower computational resources. Furthermore, our approach is thoroughly examined on six different edge devices under various hardware cost constraints.

翻译：硬件感知神经架构搜索（HW-NAS）是一种针对特定任务和目标硬件自动设计神经网络架构的技术。然而，评估候选架构的性能是HW-NAS中的关键挑战，因为这需要大量计算资源。为应对这一挑战，我们提出了一种高效的基于硬件感知进化的NAS方法，称为HW-EvRSNAS。该方法将神经架构搜索问题重新定义为：在满足目标硬件成本约束的前提下，寻找与参考模型性能相似的架构。这通过一种称为表示互信息（RMI）的表示相似度度量作为代理性能评估器来实现。该度量利用单个训练批次，计算参考模型与采样架构的隐藏层表示之间的互信息。我们还引入了一个惩罚项，该惩罚项根据架构的硬件成本与所需硬件成本阈值的偏离程度对搜索过程进行惩罚。与文献中最高可达8000倍加速的研究相比，这显著缩短了搜索时间，同时降低了二氧化碳排放。该方法在两种不同搜索空间下使用较低计算资源进行评估。此外，我们在六种不同边缘设备上，针对多种硬件成本约束条件，对所提出的方法进行了全面检验。