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Title: 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.  more » « less
Award ID(s):
2409697
PAR ID:
10591356
Author(s) / Creator(s):
; ;
Publisher / Repository:
IEEE
Date Published:
Journal Name:
Proceedings
ISSN:
2159-3477
Subject(s) / Keyword(s):
In-memory computing neural architecture search processing-in-memory memristive crossbar optimization
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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