# Multi-Objective Neural Architecture Search for In-Memory Computing

arXiv: https://arxiv.org/abs/2406.06746 · HTML: https://mlsanity.com/paper/2406.06746/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 27 references that are in the map.
- First posted on arXiv 10 Jun 2024.

## Builds on (most cited first, 8 of 8)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PUMA: A Programmable Ultra-efficient Memristor-based Accelerator for Machine Learning Inference](https://mlsanity.com/paper/1901.10351/) (2019)
- [Device-Circuit-Architecture Co-Exploration for Computing-in-Memory Neural Accelerators](https://mlsanity.com/paper/1911.00139/) (2019)
- [Uncertainty Modeling of Emerging Device-based Computing-in-Memory Neural Accelerators with Application to Neural Architecture Search](https://mlsanity.com/paper/2107.06871/) (2021)
- [An In-Memory Analog Computing Co-Processor for Energy-Efficient CNN Inference on Mobile Devices](https://mlsanity.com/paper/2105.13904/) (2021)
- [Interconnect Parasitics and Partitioning in Fully-Analog In-Memory Computing Architectures](https://mlsanity.com/paper/2201.12480/) (2022)
- [Heterogeneous Integration of In-Memory Analog Computing Architectures with Tensor Processing Units](https://mlsanity.com/paper/2304.09258/) (2023)
- [IMAC-Sim: A Circuit-level Simulator For In-Memory Analog Computing Architectures](https://mlsanity.com/paper/2304.09252/) (2023)

## Built on by (most cited first, 1 of 1)

- [CrossNAS: A Cross-Layer Neural Architecture Search Framework for PIM Systems](https://mlsanity.com/paper/2505.22868/) (2025)

## Abstract (arXiv)

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.