Multi-Objective Neural Architecture Search for In-Memory Computing
<|reference_start|>Multi-Objective Neural Architecture Search for In-Memory Computing: In this work, we employ neural architecture search (NAS) to enhance the efficiency of deploying diverse machine learning (ML) tasks on in-memory computing (IMC) architectures. Initially, we design three fundamental components inspired by the convolutional layers found in VGG and ResNet models. Subsequently, we utilize Bayesian optimization to construct a convolutional neural network (CNN) model with adaptable depths, employing these components. Through the Bayesian search algorithm, we explore a vast search space comprising over 640 million network configurations to identify the optimal solution, considering various multi-objective cost functions like accuracy/latency and accuracy/energy. Our evaluation of this NAS approach for IMC architecture deployment spans three distinct image classification datasets, demonstrating the effectiveness of our method in achieving a balanced solution characterized by high accuracy and reduced latency and energy consumption.<|reference_end|>
@article{amin2024multi-objective,
    title={Multi-Objective Neural Architecture Search for In-Memory Computing},
    author={Md Hasibul Amin, Mohammadreza Mohammadi, Ramtin Zand},
    journal={arXiv preprint arXiv:2406.06746},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.06746},
    primaryClass={cs.LG cs.ET}
}