Towards Leveraging AutoML for Sustainable Deep Learning: A Multi-Objective HPO Approach on Deep Shift Neural Networks
arXiv:2404.01965v3 Announce Type: replace-cross Abstract: Deep Learning (DL) has advanced various fields by extracting complex patterns from large datasets. However, the computational demands of DL models pose environmental and resource challenges. Deep shift neural networks (DSNNs) offer a solution by leveraging shift operations to reduce computational complexity at inference. Following the insights from standard DNNs, we are interested in leveraging the full potential of DSNNs by means of AutoML techniques. We study the impact of hyperparameter optimization (HPO) to maximize DSNN performance
The increasing computational demands of advanced Deep Learning models are driving a critical need for more sustainable and efficient AI architectures, making research into solutions like Deep Shift Neural Networks timely.
This research explores methods to significantly reduce the computational and environmental footprint of Deep Learning, which is crucial for scaling AI globally and mitigating the energy bottleneck it is creating.
The focus shifts towards optimizing AI models not just for performance but also for energy efficiency, potentially making advanced AI more accessible and sustainable by reducing reliance on massive compute resources.
- · AI hardware manufacturers focused on efficiency
- · Energy-conscious AI developers
- · AI-dependent industries with limited compute resources
- · Cloud providers offering efficient AI services
- · Developers solely focused on large, inefficient models
- · Organizations with outdated, energy-intensive AI infrastructure
Widespread adoption of computationally efficient AI models like DSNNs for various applications.
Reduced energy consumption and carbon footprint of AI, alleviating pressure on electrical grids.
Democratization of advanced AI capabilities due to lower computational barriers and operating costs.
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Read at arXiv cs.AI