Artificial Intelligence (AI) has shown significant promise in various real-world applications, but studies have revealed disparities in performance based on the data and hardware systems used for training and deployment. For example, AI tools for facial recognition have shown biases towards fair-skinned individuals over dark-skinned individuals. As a result, efforts have been made to enhance the fairness of AI models, including exploring the influence of hardware systems on AI fairness.
Researchers at the University of Notre Dame conducted a study to investigate the impact of hardware systems on the fairness of AI. Their research, published in Nature Electronics, focused on emerging computing-in-memory (CiM) devices and their influence on Deep Neural Networks (DNNs). The study aimed to fill the gap in the existing literature by examining the relationship between hardware designs and AI fairness.
The research team conducted two main types of experiments. The first set of experiments explored the effects of hardware-aware neural architecture designs on fairness. The findings suggested that larger, more complex neural networks tended to exhibit greater fairness, but they posed challenges in deployment on resource-constrained devices. The researchers proposed strategies to compress larger models to maintain performance while reducing computational load.
The second set of experiments focused on non-idealities in hardware systems, such as device variability and stuck-at-fault issues in CiM architectures. The results demonstrated trade-offs between accuracy and fairness based on different hardware setups. To address these challenges, the researchers recommended employing noise-aware training strategies to enhance robustness and fairness without significantly increasing computational demands.
The study highlighted the critical role of hardware in influencing the fairness of AI models. It emphasized the need for developers to consider both software algorithms and hardware platforms when designing AI systems for sensitive applications, such as medical diagnostics. By focusing on hardware-aware design strategies, developers can create AI systems that are accurate, equitable, and capable of analyzing data from users with diverse characteristics.
The research team plans to continue exploring the intersection of hardware design and AI fairness. They aim to develop cross-layer co-design frameworks that optimize neural network architectures for fairness while considering hardware constraints. Additionally, they intend to devise adaptive training techniques to address hardware variability and limitations, ensuring fair AI deployments across different devices and scenarios. By investigating how specific hardware configurations can be tuned to enhance fairness, the researchers hope to pave the way for the development of new classes of devices with fairness as a primary objective in AI systems.
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