Abstract
Neural Architecture Search (NAS) is a popular tool for automatically generating Neural Network (NN) architectures. In early NAS works, these tools typically optimized NN architectures for a single metric, such as accuracy. However, in the case of resource constrained Machine Learning, one single metric is not enough to evaluate a NN architecture. For example, a NN model achieving a high accuracy is not useful if it does not fit inside the flash memory of a given system. Therefore, recent works on NAS for resource constrained systems have investigated various approaches to optimize for multiple metrics. In this paper, we propose that, on top of these approaches, it could be beneficial for NAS optimization of resource constrained systems to also consider input data granularity. We name such a system “Data Aware NAS”, and we provide experimental evidence of its benefits by comparing it to traditional NAS.
Original language | English |
---|---|
Title of host publication | Proceedings of tinyML Research Symposium |
Number of pages | 7 |
Publication status | Accepted/In press - 2023 |
Event | tinyML Research Symposium’23 - Burlingame, United States Duration: 27 Mar 2023 → 27 Mar 2023 |
Conference
Conference | tinyML Research Symposium’23 |
---|---|
Country/Territory | United States |
City | Burlingame |
Period | 27/03/2023 → 27/03/2023 |
Keywords
- Neural Architecture Search
- Resource Constrained Machine Learning
- tinyML
- Convolutional Neural Networks
- Data Granularity