Hardware-aware Neural Architecture Search approaches (HW-NAS) automate the
design of deep learning architectures, tailored specifically to a given target
hardware platform. Yet, these techniques demand substantial computational
resources, primarily due to the expensive process of assessing the performance
of identified architectures. To alleviate this problem, a recent direction in
the literature has employed representation similarity metric for efficiently
evaluating architecture performance. Nonetheless, since it is inherently a
single objective method, it requires multiple runs to identify the optimal
architecture set satisfying the diverse hardware cost constraints, thereby
increasing the search cost. Furthermore, simply converting the single objective
into a multi-objective approach results in an under-explored architectural
search space. In this study, we propose a Multi-Objective method to address the
HW-NAS problem, called MO-HDNAS, to identify the trade-off set of architectures
in a single run with low computational cost. This is achieved by optimizing
three objectives: maximizing the representation similarity metric, minimizing
hardware cost, and maximizing the hardware cost diversity. The third objective,
i.e. hardware cost diversity, is used to facilitate a better exploration of the
architecture search space. Experimental results demonstrate the effectiveness
of our proposed method in efficiently addressing the HW-NAS problem across six
edge devices for the image classification task.

提出了一种名为 MO-HDNAS 的多目标方法，通过优化三个目标（最大化表示相似度度量、最小化硬件成本和最大化硬件成本的多样性）在单次运行中以低计算成本的方式，识别出一组权衡的架构，有效地解决了适用于图像分类任务的硬件感知神经架构搜索问题。

多目标硬件感知神经架构搜索利用硬件成本多样性

Multi-Objective Hardware Aware Neural Architecture Search using Hardware  Cost Diversity

Hardware-aware Neural Architecture Search (HW-NAS) is increasingly being used
to design efficient deep learning architectures. An efficient and flexible
search space is crucial to the success of HW-NAS. Current approaches focus on
designing a macro-architecture and searching for the architecture's
hyperparameters based on a set of possible values. This approach is biased by
the expertise of deep learning (DL) engineers and standard modeling approaches.
In this paper, we present a Grassroots Operator Search (GOS) methodology. Our
HW-NAS adapts a given model for edge devices by searching for efficient
operator replacement. We express each operator as a set of mathematical
instructions that capture its behavior. The mathematical instructions are then
used as the basis for searching and selecting efficient replacement operators
that maintain the accuracy of the original model while reducing computational
complexity. Our approach is grassroots since it relies on the mathematical
foundations to construct new and efficient operators for DL architectures. We
demonstrate on various DL models, that our method consistently outperforms the
original models on two edge devices, namely Redmi Note 7S and Raspberry Pi3,
with a minimum of 2.2x speedup while maintaining high accuracy. Additionally,
we showcase a use case of our GOS approach in pulse rate estimation on
wristband devices, where we achieve state-of-the-art performance, while
maintaining reduced computational complexity, demonstrating the effectiveness
of our approach in practical applications.

我们提出了一种基于数学基础构造新的高效运算符以降低计算复杂度并保持模型精度的 Grassroots 操作符搜索方法，并在各种深度学习模型和边缘设备上展示了其在提高性能和实用应用中的有效性。