Summary of Multi-objective Neural Architecture Search For In-memory Computing, by Md Hasibul Amin et al.
Multi-Objective Neural Architecture Search for In-Memory Computing
by Md Hasibul Amin, Mohammadreza Mohammadi, Ramtin Zand
First submitted to arxiv on: 10 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Emerging Technologies (cs.ET)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A neural architecture search (NAS) technique is employed to optimize machine learning (ML) task deployment on in-memory computing (IMC) architectures. The approach involves designing three fundamental components inspired by VGG and ResNet models, and then using Bayesian optimization to construct a convolutional neural network (CNN) model with adaptable depths. This method explores over 640 million network configurations to identify the optimal solution considering multi-objective cost functions like accuracy/latency and accuracy/energy. The effectiveness of this approach is evaluated on three image classification datasets, demonstrating high accuracy and reduced latency and energy consumption. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In-memory computing (IMC) makes it faster and more efficient to do certain tasks with computers. To make IMC work better for machine learning, researchers are trying new ways to design computer networks. They use something called neural architecture search (NAS), which is like a super-smart computer program that can try many different designs until it finds the best one. This team used NAS to create a special kind of computer network that works really well on IMC. They tested it with lots of different pictures and found that it was very good at recognizing what was in each picture, while also using less power and time. |
Keywords
» Artificial intelligence » Cnn » Image classification » Machine learning » Neural network » Optimization » Resnet
