Multi-Objective Neural Architecture Search for In-Memory Computing

• URL: http://arxiv.org/abs/2406.06746v1
• Date: Mon, 10 Jun 2024 19:17:09 GMT
• Title: Multi-Objective Neural Architecture Search for In-Memory Computing
• Authors: Md Hasibul Amin, Mohammadreza Mohammadi, Ramtin Zand,
• Abstract summary: We employ neural architecture search (NAS) to enhance the efficiency of deploying diverse machine learning (ML) tasks on in-memory computing architectures. Our evaluation of this NAS approach for IMC architecture deployment spans three distinct image classification datasets.
• Score: 0.5892638927736115
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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