Designing resource-constrained neural networks using neural architecture search targeting embedded devices

Cassimon, Thomas; Vanneste, Simon; Bosmans, Stig; Mercelis, Siegfried; Hellinckx, Peter

doi:10.1016/j.iot.2020.100234

Cited by 10 publications

(10 citation statements)

References 18 publications

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“…Artificial intelligence (AI) has been proven of their feasibility in capturing nonlinear relationships. Artificial intelligence has been widely used in many fields (Cassimon et al 2020;Guo et al 2020;He et al 2014), and AI can quickly diagnose COVID-19 (Zhang et al 2020). To overcome limitations of the epidemiological model, we develop artificial intelligence (AI) for real-time predicting of the new confirmed cases of COVID-19 all over the world.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the confirmed cases and deaths of global COVID-19 using artificial intelligence

Guo

2021

Environ Sci Pollut Res

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Section: Introductionmentioning

confidence: 99%

Prediction of the confirmed cases and deaths of global COVID-19 using artificial intelligence

Guo

2021

Environ Sci Pollut Res

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“…The agent who is looking for the answer to the optimization problem tries to find the goal by gaining the most possible rewards in an interaction with the environment. In the scope of NAS, an RL agent interacts with the neural network environment in the SSp of the NAS algorithm [15,17,60] and learns to find the optimized architecture based on feedback that can be one of several or more evaluation metrics, such as accuracy, complexity, latency, etc. For instance, in [15], the problem was defined as an RL agent that tries to optimize a specific objective like accuracy by selecting architectural components.…”

Section: Reinforcement Learningmentioning

confidence: 99%

Systematic Review on Neural Architecture Search

Avval,

Yaghoubi,

Eskue

et al. 2024

Preprint

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Machine Learning (ML) has revolutionized various fields, enabling the development of intelligent systems capable of solving complex problems. However, the process of manually designing and optimizing ML models is often timeconsuming, labor-intensive, and requires specialized expertise. To address these challenges, Automatic Machine Learning (AutoML) has emerged as a promising approach that automates the process of selecting and optimizing ML models. Within the realm of AutoML, Neural Architecture Search (NAS) has emerged as a powerful technique that automates the design of neural network architectures, the core components of ML models. It has recently gained significant attraction due to its capability to discover novel and efficient architectures that surpass human-designed counterparts. This manuscript aims to present a systematic review of the literature on this topic published between 2017 and 2023 to identify, analyze, and classify the different types of algorithms developed for NAS. The methodology follows the guidelines of Systematic Literature Review (SLR) methods. Consequently, this study identified 160 articles that provide a comprehensive overview of the field of NAS, encompassing discussion on current works, their purposes, conclusions, and predictions of the direction of this science branch in its main core pillars: Search Space (SSp), Search Strategy (SSt), and Validation Strategy (VSt). Subsequently, the key milestones and advancements that have shaped the field are highlighted. Moreover, we discuss the challenges and open issues that remain in the field. We envision that NAS will continue to play a pivotal role in the advancement of ML, enabling the development of more intelligent and efficient ML models for a wide range of applications.

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“…On the issue of NAS search space, we found the work of Cassimon et al . 42 which uses a cell-based representation approach for search space very interesting. The method adapted reinforcement learning to optimise cells for detecting the two types of networks, namely Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN).…”

Section: Overview Of Eosa and Review Of Related Studiesmentioning

confidence: 99%

A bioinspired neural architecture search based convolutional neural network for breast cancer detection using histopathology images

Oyelade

Ezugwu

2021

Sci Rep

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The design of neural architecture to address the challenge of detecting abnormalities in histopathology images can leverage the gains made in the field of neural architecture search (NAS). The NAS model consists of a search space, search strategy and evaluation strategy. The approach supports the automation of deep learning (DL) based networks such as convolutional neural networks (CNN). Automating the process of CNN architecture engineering using this approach allows for finding the best performing network for learning classification problems in specific domains and datasets. However, the engineering process of NAS is often limited by the potential solutions in search space and the search strategy. This problem often narrows the possibility of obtaining best performing networks for challenging tasks such as the classification of breast cancer in digital histopathological samples. This study proposes a NAS model with a novel search space initialization algorithm and a new search strategy. We designed a block-based stochastic categorical-to-binary (BSCB) algorithm for generating potential CNN solutions into the search space. Also, we applied and investigated the performance of a new bioinspired optimization algorithm, namely the Ebola optimization search algorithm (EOSA), for the search strategy. The evaluation strategy was achieved through computation of loss function, architectural latency and accuracy. The results obtained using images from the BACH and BreakHis databases showed that our approach obtained best performing architectures with the top-5 of the architectures yielding a significant detection rate. The top-1 CNN architecture demonstrated a state-of-the-art performance of base on classification accuracy. The NAS strategy applied in this study and the resulting candidate architecture provides researchers with the most appropriate or suitable network configuration for using digital histopathology.

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Designing resource-constrained neural networks using neural architecture search targeting embedded devices

Cited by 10 publications

References 18 publications

Prediction of the confirmed cases and deaths of global COVID-19 using artificial intelligence

Prediction of the confirmed cases and deaths of global COVID-19 using artificial intelligence

Systematic Review on Neural Architecture Search

A bioinspired neural architecture search based convolutional neural network for breast cancer detection using histopathology images

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