In this work, we present TinyTNAS, a novel hardware-aware multi-objective Neural Architecture Search (NAS) tool specifically designed for TinyML time series classification. Unlike traditional NAS methods that rely on GPU capabilities, TinyTNAS operates efficiently on CPUs, making it accessible for a broader range of applications. Users can define constraints on RAM, FLASH, and MAC operations to discover optimal neural network architectures within these parameters. Additionally, the tool allows for time-bound searches, ensuring the best possible model is found within a user-specified duration. By experimenting with benchmark dataset UCI HAR, PAMAP2, WISDM, MIT BIH, and PTB Diagnostic ECG Databas TinyTNAS demonstrates state-of-the-art accuracy with significant reductions in RAM, FLASH, MAC usage, and latency. For example, on the UCI HAR dataset, TinyTNAS achieves a 12x reduction in RAM usage, a 144x reduction in MAC operations, and a 78x reduction in FLASH memory while maintaining superior accuracy and reducing latency by 149x. Similarly, on the PAMAP2 and WISDM datasets, it achieves a 6x reduction in RAM usage, a 40x reduction in MAC operations, an 83x reduction in FLASH, and a 67x reduction in latency, all while maintaining superior accuracy. Notably, the search process completes within 10 minutes in a CPU environment. These results highlight TinyTNAS's capability to optimize neural network architectures effectively for resource-constrained TinyML applications, ensuring both efficiency and high performance. The code for TinyTNAS is available at the GitHub repository and can be accessed at https://github.com/BidyutSaha/TinyTNAS.git.

本研究提出了TinyTNAS，一种专门为TinyML时间序列分类设计的硬件感知多目标神经架构搜索工具，解决了传统方法对GPU依赖的问题。它允许用户在给定的内存、FLASH和MAC操作限制内，快速找到最佳网络架构，并在10分钟内完成搜索，同时在RAM、FLASH和MAC的使用上均显著降低。TinyTNAS展示了在资源受限环境中优化神经网络架构的能力，确保了高效能与性能。

TinyTNAS：无GPU、时间限制、硬件感知的神经架构搜索用于TinyML时间序列分类