In this paper, we present an improvement on a recent NAS method, Efficient Neural Architecture Search (ENAS). We adapt ENAS to not only take into account the ...
Designing resource-constrained neural networks using neural ...
www.sciencedirect.com › article › pii
In this paper, we present an improvement on a recent NAS method, Efficient Neural Architecture Search (ENAS). We adapt ENAS to not only take into account the ...
In this paper, we present an improvement on a recent NAS method, Efficient Neural Architecture Search (ENAS). We adapt ENAS to not only take into account the ...
In this paper, we present an improvement on a recent NAS method, Efficient Neural Architecture Search (ENAS). We adapt ENAS to not only take into account the ...
Dec 1, 2020 · Abstract. Recent advances in the field of Neural Architecture Search (NAS) have made it possible to develop state-of-the-art deep learning.
Designing resource-constrained neural networks using neural architecture search targeting embedded devices ; dc.contributor.author, Cassimon, Thomas ; dc.
The design of neural network architectures is frequently either based on human expertise using trial/error and em- pirical feedback or tackled via large ...
Jul 3, 2023 · In Section 5, we will discuss the search algorithm for hardware-constrained NAS methods. In Section 6, we discuss the specific constraints of ...
Apr 7, 2024 · We focus on resource-efficient inference based on deep neural networks (DNNs), the predominant machine learning models of the past decade.
We build a neural architecture search (NAS) system, called μNAS, to automate the design of such small-yet-powerful MCU-level networks.