Neural Architecture Search (NAS) effectively discovers new Convolutional
Neural Network (CNN) architectures, particularly for accuracy optimization.
However, prior approaches often require resource-intensive training on super
networks or extensive architecture evaluations, limiting practical
applications. To address these challenges, we propose MicroNAS, a
hardware-aware zero-shot NAS framework designed for microcontroller units
(MCUs) in edge computing. MicroNAS considers target hardware optimality during
the search, utilizing specialized performance indicators to identify optimal
neural architectures without high computational costs. Compared to previous
works, MicroNAS achieves up to 1104x improvement in search efficiency and
discovers models with over 3.23x faster MCU inference while maintaining similar
accuracy

MicroNAS 是一种为边缘计算中的微控制器单元（MCU）设计的硬件感知的零样本神经架构搜索框架，通过考虑目标硬件的优化性能，在不高昂的计算成本下确定最佳神经架构，并实现了近 1104 倍的搜索效率提升和超过 3.23 倍的 MCU 推断速度提升，同时保持相似的准确性。

MicroNAS: 零样本神经架构搜索用于 MCUs

MicroNAS: Zero-Shot Neural Architecture Search for MCUs

This paper presents MicroNAS, a system designed to automatically search and
generate neural network architectures capable of classifying time series data
on resource-constrained microcontrollers (MCUs) and generating standard tf-lite
ML models. MicroNAS takes into account user-defined constraints on execution
latency and peak memory consumption on a target MCU. This approach ensures that
the resulting neural network architectures are optimised for the specific
constraints and requirements of the MCU on which they are implemented. To
achieve this, MicroNAS uses a look-up table estimation approach for accurate
execution latency calculations, with a minimum error of only 1.02ms. This
accurate latency estimation on MCUs sets it apart from other hardware-aware
neural architecture search (HW-NAS) methods that use less accurate estimation
techniques. Finally, MicroNAS delivers performance close to that of
state-of-the-art models running on desktop computers, achieving high
classification accuracies on recognised datasets (93.93% on UCI-HAR and 96.33%
on SkodaR) while running on a Cortex-M4 MCU.

MicroNAS 是一个系统，能够自动搜索和生成神经网络体系结构，用于分类时间序列数据，应用于资源受限的微控制器 (MCUs)，并生成标准 tf-lite ML 模型。