  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.
